REVERSIBLE DATA HIDING USING CHAOTIC AND 2D LOGISTIC ... · BONAFIDE CERTIFICATE Certified that...

REVERSIBLE DATA HIDING USING CHAOTIC

AND 2D LOGISTIC ENCRYPTION

PROJECT REPORT

PHASE-II

Submitted by

DHASHARATHIR

Register No 14MCO008

in partial fulfillment for the requirement of award of the degree

of

MASTER OF ENGINEERING

in

COMMUNICATION SYSTEMS

Department of Electronics and Communication Engineering

KUMARAGURU COLLEGE OF TECHNOLOGY

(An autonomous institution affiliated to Anna University Chennai)

COIMBATORE - 641 049

ANNA UNIVERSITY CHENNAI 600 025

APRIL - 2016

BONAFIDE CERTIFICATE

Certified that this project report titled ldquoREVERSIBLE DATA HIDING USING

CHAOTIC AND 2D LOGISTIC ENCRYPTIONrdquo is the bonafide work of

DHASHARATHIR [Reg No 14MCO008] who carried out the research under my

supervision Certified further that to the best of my knowledge the work reported herein

does not form part of any other project or dissertation on the basis of which a degree or

award was conferred on an earlier occasion on this or any other candidate

SIGNATURE SIGNATURE DrAAmsaveni Dr AVasuki

PROJECT SUPERVISOR HEAD OF THE DEPARTMENT Department of ECE Department of ECE Kumaraguru College of Technology Kumaraguru College of Technology COIMBATORE - 641049 COIMBATORE -641049

The Candidate with university Register No 14MCO008 was examined by us in

the project viva ndashvoice examination held on

INTERNAL EXAMINER EXTERNAL EXAMINER

ii

ACKNOWLEDGEMENT

First I would like to express my praise and gratitude to the Lord who has

showered his grace and blessings enabling me to complete this project in an excellent

manner

I express my sincere thanks to the management of Kumaraguru College of

Technology and Joint Correspondent Shri Shankar Vanavarayar for his kind support

and for providing necessary facilities to carry out the work

I would like to express my sincere thanks to our beloved Principal DrRSKumar

PhD Kumaraguru College of Technology who encouraged me with his valuable

thoughts

I would like to thank DrAVasuki PhD Head of the Department Electronics

and Communication Engineering for her kind support and for providing necessary

facilities to carry out the project work

I wish to thank with everlasting gratitude to the project coordinator

DrMAlagumeenaakshi PhD Asst Professor(SRG) Department of Electronics and

Communication Engineering throughout the course of this project work

I am greatly privileged to express my heartfelt thanks to my project guide

DrAAmsaveni PhD Associate Professor Department of Electronics and

Communication Engineering for her expert counseling and guidance to make this project

to a great deal of success and I wish to convey my deep sense of gratitude to all teaching

and non-teaching staffs of ECE Department for their help and cooperation

Finally I thank my parents and my family members for giving me the moral

support and abundant blessings in all of my activities and my dear friends who helped me

to endure my difficult times with their unfailing support and warm wishes

iii

ABSTRACT

Data hiding is a technique used in the field of information security Using this

technique secret data can be embedded inside a cover medium by the sender and the

secret data and cover medium can be extracted without any distortion by the receiver The

main benefit of this technique is that the cover medium used for embedding can also be

recovered with high quality Data hiding has a wide range of applications such as medical

image sharing multimedia archive management image transcoding video error

concealment and military application According to the problems of steganography the

main effort is to provide a better imperceptibility of stego-image that can be done by

decreasing distortion of image

The proposed method provides a data hiding technique based on Chaotic and 2D

Logistic encryption The cover image is divided into a number of blocks and 2D logistic

map is created The data to be embedded is encrypted using chaos encryption technique

Finding the best location to hide the secret data is an important task so that it will conceal

the existence of the message The optimal location to hide the secret data is embedding

on the LSB bits This results in a stego image which is not only good in quality but is also

able to sustain certain noise After embedding the secret data into cover medium the

symmetric key is applied The resultant binary pixels are converted into DNA sequence

for additional level of security Attacks have been applied on the image The reverse

process is done to convert the stego image into original image and data can also be

extracted This proposed method ensures the three essential properties which are

commonly used to determine quality of data hiding scheme They are imperceptibility

robustness reversibility and security

The performance metrics like Peak Signal to Noise Ratio (PSNR) Mean Square Error

(MSE) Average Difference (AD) Structural Content (SC) Laplacian Mean Squared

Error (LMSE) Normalized Absolute Error (NAE) and Normalized Correlation

Coefficient (NCC) have been evaluated

iv

TABLE OF CONTENTS

CHAPTER NO TITLE PAGE NO

ABSTRACT iv

LIST OF FIGURES vii

LIST OF TABLES viii

LIST OF ABBRIVEATION viii

1 INTRODUCTION 1

11Cryptography 1

12 Steganography 3

13 Reversible Data Hiding 5

2 LITERATURE SURVEY 6

3 PROPOSED METHODOLOGY 14

31 Chaotic Encryption 15

32 The RSA Algorithm 15

321 Key Generation 16

322 Encryption 16

323 Decryption 16

33 2D Logistic Encryption 17

34 DNA Sequence 18

35 Attacks 19

351 Shearing 20

352 Image Scaling 20

353 Rotation 21

354 Colour Reduced Image 21

355 Blur Image 22

356 Flipped Image 23

357 Cropped Image 23

358 Intensity Transformation Image 24

359 Sharpening 25

3510 Gaussian Noise and Median Filtering 25

3511 Histogram of Contrast Image 26

3512 Speckle Noise and Median Filtering 27

36 Proposed Algorithm 28

4 RESULTS AND DISCUSSIONS 295

5 CONCLUSION AND FUTURE WORK 35

51 Conclusion 35

52 Future Work 35

REFERENCES 36

LIST OF PUBLICATIONS 38

vi

LIST OF TABLES

PAGE NO

41 Performance Metric Calculation 33

42 Performance Metric Calculation between original and 34 recovered Barbara image

LIST OF ABBREVIATIONS

2D 2 Dimensional

AD Average Difference

BER Bit Error Rate

LMSE Laplacian Mean Square Error

LSB Least Significant Bit

MD Maximum Difference

MSE Mean Square Error

NCC Normalized Cross Correlation

PSNR Peak Signal to Noise Ratio

SC Structural Content

viii

TABLE TITLE

NO

2

vii

LIST OF FIGURES

FIGURE

NO

CAPTION

PAGE

NO

11 Symmetric-key cryptography 2

12 Public key Cryptography 2

13 Categories of Image Steganography 4

14 Reversible Data Hiding System 5

31 Work Flow Diagram 14

32 Shearing Image 20

33 Scaling Image 20

34 Rotation image 21


36 Blur Image 22

37 Flipped Image 23

38 Cropped Image 24


310 Sharpened Image 25

311 Gaussian Noise and Median Filter Image 26

312 Contrast Image 26


314 Speckle Noise and Median Filter Image 27

41 Gray Scale Cover image of size 256times256 (a) Barbara (b) Boat

(c) Butterfly (d) Charlie Chaplain (e) Lena

31

42 Input Image and 2D Logistic Encrypted Image 32

43 DNA Sequence 32

44 Recovered Image 33

44 Recovered Text 33

2

1

CHAPTER 1

INTRODUCTION

In an information sharing environment security of information plays an important

role Some information that is sensitive or confidential in nature must be kept private

With the introduction of computers the need for automated tools for protecting files and

other information stored in the computer become evident Transmission of sensitive

information via an open internet channel increases the risk of interception There are

many techniques proposed to deal with this issue They are

1) Cryptography

2) Steganography

3) Reversible Data Hiding

11 CRYPTOGRAPHY

Cryptography is the practice and study of techniques for secure communication in

the presence of third parties (called adversaries) More generally it is about constructing

and analyzing protocols that overcome the influence of adversaries This technique alters

the form of the message at the sender and transmits it At the receiver the original

message is extracted It mainly involves 2 operations

Encryption It is the process of the conversion of information from a readable state to

apparent nonsense with the usage of a key It is done by the sender

Decryption It is the reverse process of encryption That is it is the process of converting

scrambled message into the original one with the help of key The key may be similar to

the one which is used in encryption or it may be a different one It is done at the receiver

side

The cryptography is characterized by 3 independent dimensions

2

1) The type of operations used for transforming Plaintext to Cipher text

All encryption algorithms are based on two general principles They are

substitution and transposition Substitution is the one in which each element in the plain

text is transformed into another element Transposition is the one in which elements in

the plain text are rearranged The fundamental condition is that no information be lost

2) The Number of keys used

Based on this we can classify the techniques into two

a) Symmetric-key Cryptography Symmetric-key cryptography refers to encryption

methods in which both the sender and receiver share the same key (or less commonly in

which their keys are different but related in an easily computable way)

Figure 11 Symmetric-key cryptography

b) Public key Cryptography In public-key cryptosystems the public key may be freely

distributed while its paired private key must remain secret In a public-key encryption

system the public key is used for encryption while the private or secret key is used for

decryption

Figure 12 Public key Cryptography

3

3) The way in which the plaintext is processed

There are 2 types

a) Block Cipher It processes the input one block of elements at a time producing an

output block for each input block

b) Stream Cipher It processes the input elements continuously producing output one

element at a time as it goes along

12 STEGANOGRAPHY

It is the art and science of encoding hidden messages in such a way that no one

apart from the sender and intended recipient suspects the existence of the message It is a

form of security through obscurity Generally the hidden messages will appear to be (or

be part of) something else images articles shopping lists or some other cover texts

Plainly visible encrypted messages no matter how unbreakable will arouse interest and

may in themselves be incriminating in countries where encryption is illegal For example

the hidden message may be in invisible ink between the visible lines of a private letter

The advantage of steganography over cryptography alone is that the intended secret

message does not attract attention to itself as an object of scrutiny So cryptography is the

practice of protecting the contents of a message alone steganography is concerned with

concealing the fact that a secret message is being sent as well as concealing the contents

of the message Steganography includes the concealment of information within computer

files In digital steganography electronic communications may include steganographic

coding inside of a transport layer such as a document file image file program or

protocol Media files are ideal for steganographic transmission because of their large size

There has been a rapid growth of interest in steganography for two main reasons

(i) The publishing and broadcasting industries have become interested in techniques for

hiding encrypted copyright marks and serial numbers in digital films audio

recordings books and multimedia products

(ii) Moves by various governments to restrict the availability of encryption services

have motivated people to study methods by which private messages can be

4

embedded in seemingly innocuous cover messages

Fig 13 Categories of Image Steganography

There are many applications for digital steganography of image including

copyright protection feature tagging and secret communication Copyright notice or

watermark can embedded inside an image to identify it as intellectual property If

someone attempts to use this image without permission we can prove by extracting the

watermark In feature tagging captions annotations time stamps and other descriptive

elements can be embedded inside an image Copying the stegondashimage also copies of the

embedded features and only parties who posses the decoding stego-key will be able to

extract and view the features On the other hand secret communication does not advertise

a covert communication by using steganography Therefore it can avoid scrutiny of the

sender message and recipient This is effective only if the hidden communication is not

detected by the others people In general steganography is two types reversible and

irreversible

5

13 Reversible Data Hiding

Figure 14 Reversible Data Hiding System

Secret Message The secret message or information to hide

Cover File Digital Medium The data or medium which concealed the secret message

Stego File A modified version of cover that contains the secret message

Key Additional secret data that is needed for the embedding and extracting processes

and must be known to both the sender and the recipient

Steganographic Method A steganographic function that takes cover secret message

and key as parameters and produces stego as output

Inverse of Steganographic Method A steganographic function that has stego and key

as parameters and produces secret message as output This is the inverse of method used

in embeding process in the sense that the result of the extracting process is identical to the

input of the embedding process

6

CHAPTER 2

LITERATURE SURVEY

1) Nidhi Sethi Deepika Sharma (2014) ldquoA Novel Method Of Image

Encryption using Logistic Mappingrdquo International Journal of Computer

Science Engineering (IJCSE)

This paper presents a new method to develop secure image-encryption techniques

using a logistics based encryption algorithm In this technique a Haar wavelet transform

was used to decompose the image and decorrelate its pixels into averaging and

differencing components The logistic based encryption algorithm produces a cipher of

the test image that has good diffusion and confusion properties The remaining

components (the differencing components) are compressed using a wavelet transform

Many test images are used to demonstrate the validity of the proposed algorithm The

results of several experiments show that the proposed algorithm for image cryptosystems

provides an efficient and secure approach to real-time image encryption and transmission

To send the keys in secure form steganography will be used Steganographic techniques

allow one party to communicate information to another party without a third party even

knowing that the communication is occurring

Advantages

(i) Efficient approach

(ii) Secure key transmission

(iii) Better image quality

7

2) Jing Wang Guoping Jiang (2013) ldquoA Self-Adaptive Parallel Encryption

Algorithm Based on Discrete 2D-Logistic Maprdquo International Journal of

Modern Nonlinear Theory and Application

A self-adaptive parallel encryption algorithm based on discrete 2D-Logistic

map is developed according to the position scrambling and diffusion of multi-direction in

variable space of spatial chaos The binary sequences b1b2b3bn are obtained according

to the user key in which the binary sequence 0 and 1 denote distribution mode of

processors and the number of binary sequence n denotes cycle number Then the

pseudorandom 2D matrix is generated by 2D-Logistic map and adaptive segmentation is

applied in image matrix and pseudorandom matrix according to the value and the number

of binary sequence The parallel operation is used among blocks to improve efficiency

and meet real-time demand in transmission processes However the pixel permutation is

applied in partitioned matrix through ergodic matrix generated by pseudo-random matrix-

block to decrease the correlation of adjacent pixels Then the pixel substitution is used for

fully diffusing through cipher block chaining mode until n cycles The proposed

algorithm can meet the three requirements of parallel operation in image encryption and

the real-time requirement in transmission processes The security is proved by theoretical

analysis and simulation results

Advantages

1Security is provided

2Effeciency is improved

8

3) LJani Anbarasi GSAnandha Mala Modigari Narendra (2014) ldquoDNA

based Multi-Secret Image Sharingrdquo International Conference on

Information and Communication Technologies (ICICT)

Multiple secret sharing algorithm using the YCH scheme combined with

DNA encoding is proposed focusing at better security Firstly DNA encoding for

multiple images is carried out then the addition of these encoded components by DNA is

performed Secondly the (t n) scheme used the Lagrange interpolation polynomial to

share these DNA scrambled matrices is performed bdquot‟ denotes threshold value and bdquon‟

denotes the number of participants The resulting scrambled images are encrypted into n

shares using Shamir‟s polynomial Thirdly these shares are embedded using a modular

operation Finally bdquot‟ or more shares are pooled which reconstructs the scrambled

matrices and by decoding the DNA scrambled matrices multiple secrets are

reconstructed without loss The simulation results and the security analysis prove that this

algorithm is perfect and produces results with better PSNR value The correlation co-

efficient shows that this also has the ability of resisting various attacks

Advantages

1Security is better

2Resistance against Attack

9

4)Della Babya Jitha Thomasa Gisny Augustinea Elsa Georgea Neenu

Rosia Michaela (2014) ldquoA Novel DWT based Image Securing Method using

Steganographyrdquo International Conference on Information and

Communication Technologies (ICICT)

Steganography is a data hiding technique that is widely used in various

information securing applications Steganography transmits data by hiding the existence

of the message so that a viewer cannot identify the transmission of message and hence

not able to decrypt it This work proposes a data securing technique that is used for

hiding multiple color images into a single color image using the Discrete Wavelet

Transform The cover image is split up into R G and B planes Secret images are

embedded into these planes An N-level decomposition of the cover image and the secret

images are done and some frequency components of the same are combined Secret

images are then extracted from the stego image Here the stego image obtained has a less

perceptible changes compared to the original image with high overall security

Advantages

1Less perceptible changes

2Overall security is high

10

5)Prasenjit Dasa Subhrajyoti Deba Nirmalya Kara Baby

Bhattacharyaa(2014)ldquoAn Improved DNA based Dual Cover



Dual cover steganography is an evolving technique in the field of covert

data transmission This paper focuses on the concept of using a theoretical single stranded

DNA (ssDNA) as a primary cover which is extracted from an inconspicuous cover

image They have analyzed the security loopholes and performance issues of the existing

algorithm and proposed an improved algorithm on the same basisthey use 2D Logistic

map for encrypting the cover imageThen overall encryption is RC43 types of encryption

is generally used Performance of both the algorithms are tested against several visual

and statistical attacks and parameterized in terms of both security and capacity The

comparison shows that the proposed improvements provide better overall security

Advantages

1 Robustness against various attack

2 Performance measure are calculated

3 Data hiding improves security

11

6) Bhavana Agrawal Himani Agrawal ( May-2013) ldquoImplementation of AES

and RSA Using Chaos systemrdquo International Journal of Scientific amp

Engineering Research Vol 4 No 5

This paper presents two cryptographic algorithm AES and RSA Using Chaos

Chaos has attracted much attention in the field of cryptography It describes a system

which is sensitive to initial condition It generates apparently random behavior but at the

same time is completely deterministic Chaos function is used to increase the complexity

and Security of the SystemAES and RSA are the two cryptographic algorithms In AES

we apply the Chaos on S-box where as in RSA we mix the plaintext with Chaos sequence

First then apply for encryption and decryption After Implementing AES and RSA they

compare both the technique on the basis of speed

Advantages

1Chaos function is used to improve complexity

2The speed has been improved with combined technique of AES and RSA along with

chaos technique

12

7) Pahrul Irfan Yudi Prayudi Imam Riadi ( August 2015) ldquo Image

Encryption using Combination of Chaotic System and Rivers Shamir

Adleman (RSA)rdquo International Journal of Computer Applications Vol 123

No6

Security and confidentiality of data or information at the present time has

become an important concern Advanced methods for secure transmission storage and

retrieval of digital images are increasingly needed for a number of military medical

homeland security and other applications Various kinds of techniques for increase

security data or information already is developed one common way is by cryptographic

techniques Cryptography is science to maintain the security of the message by changing

data or information into a different form so the message cannot be recognized To

compensate for increasing computing speeds increases it takes more than one encryption

algorithm to improve security of digital images One way is by using algorithms to

double cryptography do encryption and decryption Cryptographic algorithm often used

today and the proven strength specially the digital image is Algorithm with Chaos

system To improve security at the image then we use Additional algorithms namely

Rivers algorithm Shamir Adleman (RSA) which known as the standard of cryptography

algorithms This research aims to optimize security bitmap image format by combining

the two algorithms namely Chaos-based algorithms and RSA algorithm into one

application Experiments conducted show that the proposed algorithm possesses robust

security features such as fairly uniform distribution high sensitivity to both keys and

plain images almost ideal entropy and the ability to highly de-correlate adjacent pixels

in the cipher images Furthermore it has a large key space and transform image to pure

text file which greatly increases its security for image encryption

Advantages

1 It aims to optimize security bitmap image format by combining the two algorithms

namely Chaos-based algorithms and RSA algorithm into one application

13

8) Weiming Zhang KedeMa NenghaiYu(2013) ldquo Reversibility improved

data hiding in encrypted imagesrdquo School of Information Science and

Technology

A novel reversible data hiding technique in encrypted images is presented in this

paper Instead of embedding data in encrypted images directly some pixels are estimated

before encryption so that additional data can be embedded in the estimating errors A

bench mark encryption algorithm (eg AES) is applied to the rest pixels of the image and

a special encryption scheme is designed to encrypt the estimating errors Without the

encryption key one cannot get access to the original image However provided with the

data hiding key only he can embed in or extract from the encrypted image additional data

without knowledge about the original image Moreover the data extraction and image

recovery are free of errors for all images Experiments demonstrate the feasibility and

efficiency of the proposed method especially in aspect of embedding rate versus Peak

Signal-to-Noise Ratio (PSNR)

The paper proposes a novel method to significantly improve the performance by

reversing the order of encryption and vacating room In the light of this idea we empty

out room prior to image encryption by shifting the histogram of estimating errors of some

pixels and the emptied out room will be used for data hiding The proposed method is

composed of four primary steps vacating room and encrypting image data hiding in the

encrypted image data extraction and image recovery Two different schemes extraction

before decryption and decryption before extraction are raised to cope with different

applications

Advantages

(i) Achieves excellent performance in three aspects complete reversibility PSNR

under given embedding rate separability between data higher extraction and

image decryption

14

CHAPTER 3

PROPOSED METHODOLOGY

The proposed data hiding scheme aims at the security of the hidden data

Embedding is performed in spatial domain The data to be embedded is converted into

binary form from ASCII code using chaos encryption and is embedded into the cover

image obtained after 2D logistic map This embedded image is secured using symmetric

key (K1)They are converted into DNA sequence to provide additional level of security

The hidden data can be extracted from the cover image accurately with the help of

decryption techniques and secret key (K1) The cover image can also be extracted

without any distortion The fig 31 shows the workflow

Fig 31 Work Flow Diagram

SECRET DATA

COVER IMAGE

CHAOTIC

ENCRYPTION

ENCRY 2D LOGISTIC

ENCRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

CHAOTIC

DECRYPTION

ENCRY

SECRET DATA

COVER IMAGE 2D LOGISTIC

DECRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

15

31 Chaotic Encryption

Chaotic cryptography is the application of the mathematical chaos theory to the

practice of the cryptography the study or techniques used to privately and securely

transmit information with the presence of an third-party or adversary The use of chaos

or randomness in cryptography has long been sought after by entities wanting a new way

to encrypt messages However because of the lack of thorough provable security

properties and low acceptable performance chaotic cryptography has encountered

setbacksIn order to use chaos theory acceptably in cryptography they must first be

mapped to each other Properties in chaotic systems and cryptographic primitives share

unique characteristics that allow for the chaotic systems to be applied to cryptography If

chaotic parameters as well as cryptographic keys can be mapped symmetrically or

mapped to produce acceptable and functional outputs it will make it next to impossible

for an adversary to find the outputs without any knowledge the initial values Since

chaotic maps in a real life scenario require a set of numbers that are limited they may in

fact have no real purpose in a cryptosystem if the chaotic behavior can be predicted To

counter this possibility there exists simple to advanced ciphers Chaos theory used in

cryptosystems for commercial implementation has proven to be unsuccessful mainly

because a chaos theories‟ requirement to use intervals of real numbers Given enough

resources and time an adversary could be able to predict functional outcomes Since

chaotic cryptosystems have no root in number theory this would make it difficult or

impossible to implement therefore impractical

32 The RSA Algorithm

The RSA cryptosystem named after its inventors R Rivest A Shamir and L

Adleman is the most widely used public key Cryptosystem It may be used to provide

both secrecy and digital signatures and its security is based on the intractability of the

integer factorizationThe RSA algorithm involves three steps key generation encryption

and decryption

16

321 Key Generation

RSA involves a public key and a private key The public key can be known to

everyone and is used for encrypting messages Messages encrypted with the public key

can only be decrypted in a reasonable amount of time using the private key The keys for

the RSA algorithm are generated the following way To generate the two keys choose

two random large prime numbers p and q For maximum security choose p and q of

equal length Then randomly choose the encryption key e such that e and ( p minus1) (q minus1)

are relatively prime Finally use the extended Euclidean algorithm to compute the

decryption key d such that

d= e-1

mod ( (p-1) (q-1))

Note that d and n are also relatively prime The numbers e and K are the public

key the number d is the private key The two primes p and q are no longer needed They

should be discarded but never revealed

322 Encryption

Firstly receiver transmits her public key (n e) to sender and keeps the private key

secret If sender wishes to send message M to receiver Sender change the message M in

to integer m such that 0 le mltn Then sender computes the cipher text c corresponding to

Cequiv me

(mod n)

323 Decryption

Receiver can recover M from c by using private key exponent d via computing

M equiv cd

(mod n)

17

Algorithm

1Select any two prime numbers say (pq)

2Compute n=pq and also compute empty(119899)=(p-1)(q-1)

3Choose e such that 1ltelt empty(119899)

4Choose d such that (de)mod empty(119899)=1

5Public key is (en) and Private key is (dn)

6 If egt=2 then check i==1 if so return 1 else return 0

7In a iteration check for e(i)==1 if so take mod function of message with n

8Message is converted to cipher text in ASCII form with the key generated

9The cipher data in ASCII form is converted to binary form

33 2D Logistic Encryption

The chaotic system is a deterministic nonlinear system It possesses a varied

characteristics such as high sensitivity to initial conditions and system parameters

random-like behaviors and so forth Chaotic sequences produced by chaotic maps are

pseudo-random sequences their structures are very complex and difficult to be analyzed

and predicted In other words chaotic systems can improve the security of encryption

systems Thus it is advisable to encrypt digital image with chaotic systems There are

two chaotic maps one is logistic map and the other is 2D logistic map In the proposed

work 2D logistic map is used

Logistic map is an example for chaotic map and it is described as follows

x(n+1)=μ x(n)(1minusx(n))

μ is a positive constant sometimes known as the biotic potential gives the so-called

logistic map x(n) is series of values used to plot Logistic mapwhere μ isin [04] x(n) isin

(01) and n = 01 2hellip The research result shows that the system is in chaotic state

under the condition that 356994 lt μ le 4

18

2D logistic map is described in as follows

119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2

119910119894+1 = 1205832 lowast 119910119894(1 minus 119910119894) + 1205732((119909119894)2 + 119909119894 lowast 119910119894)

Where z(xy) is the logistic map 1205831 and 1205832 are positive constant sometimes known as the

biotic potential gives the so-called logistic map x be the position of images in x axis

and y be the position of images in y axis 1205731 and 1205732 are the correlation constantsWhen

275 lt μ1 le 34 275 lt μ2 le 345 015 lt 1205731le 021 and 013 lt 1205732 le 015 the system is in

chaotic state and can generate two chaotic sequences in the region (01] Due to the

system parameter γ1 and γ2 which have smaller value range we set γ1 = 017 and γ2 =

014 other parameters can be seen as secret keys

Algorithm

1A random key is generated in binary form ( 1times256) and it is stored in a array

2The random key generated is translated to map format using block processing (4times4)

3 The row and column wise transformation is carried out

4The key is now used to encrypt the cover image

52D logistic image undergoes substitution and permutation (column and row wise

shuffling is done)

34 DNA Sequence

A single DNA sequence is made up of four nucleic acid

bases A (adenine) C (cytosine) G (guanine) and T (thymine) where A and T are

complements and C and G are complements Let binary number 0 and 1 be

complements so 00 and 11 are complements and 01 and 10 are complements Thus we

can use these four bases A T G and C to encode 01 10 00 and 11 respectively The

encoding method still satisfies the Watson-Crick complement rule Usually each pixel

value of the 8 bit grey image can be expressed to 8 bits binary stream The binary stream

can be encoded to a DNA sequence whose length is 4 For example if the first pixel

19

value of the original image is 75 convert it into a binary stream [01001011] By using the

above DNA encoding rule to encode the stream we can get a DNA sequence [AGTC]

whereas we use A T G and C to express 01 10 00 and 11 respectively We can get a

binary sequence [01001011]

35 Attacks

The steganographic algorithm is used to embed secret messages into cover

image To obtain stego image while exchanging these stego-image through the public

communication channel various attacks have been made The are generally classified

into two types intentional or unintentional attacks Examples of unintentional attacks are

transmission errors lossy compression and changing the visual properties of the stego

document Intentional attacks on the other hand are deliberate attempts to distinguish

stego-objects from unmodified objects and thus detect the presence of covert

communication Attack methods generally exploit the fact that embedding information

usually changes the statistical properties of the objects compared to typical unmodified

objects In this proposed algorithm various attacks have been applied on the encrypted

image They are as follows

1)Shearing

2) Image Scaling

3) Image Rotating

4) Image color reduction

5) Image blurred

6) Image flip

7) cropping and intensity transformation

8) Image sharpening

9) Gaussian Noise and filtering

10) Image Contrast

11) Speckle Noise and Filtering

20

351 Shearing

The image is resized with the scale factor Resized image is rotated with

angle(theta)Finally spatial transformation from control point pairs is implemented

For example

Scale factor 09

Theta10

Fig 32 Shearing Image

352 Image Scaling

It resizes the image with a scale factor and rotation is performed It rotates the

image by angle (degrees) in a counterclockwise direction around its center point To

rotate the image clockwise specify a negative value for angle It makes the output image

large enough to contain the entire rotated image It uses nearest neighbour interpolation

setting the values of pixels in Output image that are outside the rotated image to 0 (zero)

For example

Scale Factor07

Theta30

scaling Image

21

Fig 33 Scaling Image

353 Rotation

It rotates the image by angle degrees in a counterclockwise direction around its

center point To rotate the image clockwise specify a negative value for angle It makes

the output image large enough to contain the entire rotated image It uses nearest

neighbour interpolation setting the values of pixels in Output image that are outside the

rotated image to 0 (zero)

For Example

Theta180

Fig 34 Rotation Image

354 Colour Reduced Image

It creates an indexed image approximation of the RGB image in the array RGB by

dithering the colors in the colormap map The colormap cannot have more than 65536

resized and rotated image

Rotated image

22

colors

For Example

Indexed image with 32 Colors

Fig 35 Colour Reduced Image

355 Blur Image

The image is blurred by using N-D filtering of multidimensional images It filters

the multidimensional array of original image with the multidimensional filter The array

of original image can be logical or a nonsparse numeric array of any class and dimension

The result image has the same size and class as of original image

Fig 36 Blur Image

Color reduced image

50 100 150 200 250 300 350 400 450 500

50

100

150

200

250

300

350

400

450

500

Blurred image

23

356 Flipped Image

It flips the image upside down Flipping is used to invert the image

Fig 37 Flipped Image

357 Cropped Image

It creates an interactive crop image tool associated with the image displayed in the

current figure called the target image The crop image tool is a movable resizable

rectangle that you can position interactively using the mouse When the crop image tool

is active the pointer changes to cross hairs when it is moved over the target image

Using the mouse image to be cropped can be specified by clicking and dragging the

mouse The crop rectangle using the mouse can be moved or resized When sizing and

positioning of the crop rectangle is finished create the cropped image by double-clicking

the left mouse button or by choosing crop image from the context menu Image cropping

returns the cropped image

Flipped image

24

Fig 38 Cropped Image

358 Intensity Transformation Adjust

It maps the intensity values in grayscale image to new values in resultant image

such that 1 of data is saturated at low and high intensity of original image This

increases the contrast of the output image

Fig 39 Intensity Transformation Image

Cropped Image

Intensity Transformation

25

359 Sharpening

Input array values outside the bounds of the array are assumed to equal the nearest

array border value The image is sharpened by using N-D filtering of multidimensional

images It filters the multidimensional array of original image with the multidimensional

filter The array of original image can be logical or a nonsparse numeric array of any

class and dimension The result image has the same size and class as of original image

Fig 310 Sharpened Image

3510 Gaussian Noise and Median Filtering

It adds Gaussian noise to the images Gaussian white noise have constant mean

and variance The noise added image is filtered using Median Filtering Median filtering

is a nonlinear operation often used in image processing to reduce salt and pepper noise

A median filter is more effective than convolution when the goal is to simultaneously

reduce noise and preserve edges Each output pixel contains the median value in the m-

by-n neighborhood around the corresponding pixel in the input image Median filter pads

the image with 0s on the edges so the median values for the points within [m n]2 of the

edges might appear distorted

Sharpened Image

26

Fig 311 Gaussian Noise and Median Filter Image

3511 Histogram of contrast image

It enhances the contrast of images by transforming the values in an intensity

image or the values in the colormap of an indexed image so that the histogram of the

output image approximately matches a specified histogram

Fig 312 Contrast Image

Gaussian Noise

Median Filtering

Contrast Image

27

Fig 313 Histogram of Contrast Image

3512 Speckle noise and Median Filtering

It adds multiplicative noise to the image I using the equation J = I+nI where n is

uniformly distributed random noise with mean 0 and variance v The default for v is 004






Fig 314 Speckle Noise and Median Filter Image

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

Histogram of Contrast Image

0 50 100 150 200 250

Speckle Noise

Median Filtering

28

36 Proposed Algorithm

Step1 Enter two keys a private key and a public key through which the RSA algorithm is

performed

Step2These key are used for encrypting the secret data using chaotic algorithm with

threshold of 2

Step3The secret data is converted into binary format from ASCII code

Step4 In the cover image every pixel intensity is taken 2D logistic encryption is applied

Step5The encryption is carried out with the key generated randomly in binary(1times256)

Step6 The 2D logistic substitution and permutation are carried out

Step7The resulting binary sequence is added with the encrypted text in LSB

Step8The image is converted to DNA sequence and transmitted

Step9 Various Attacks have been applied on the resultant image

Step10The inverse process is carried out to retrieve the original image and data

Step11The Performance Metrics have been calculated

29

CHAPTER 4

RESULTS AND DISCUSSIONS

The performance metrics of the proposed method have been evaluated

The various performance metrics are

(i) Peak Signal to Noise Ratio (PSNR)

(ii) Mean Square Error (MSE)

(iii) Structural content (SC)

(iv) Average Difference(AD)

(v) Normalized Cross Correlation(NCC)

(vi) Laplacian Mean Squared Error(LMSE)

(vii) Normalized Absolute Error(NAE)

(viii) Maximum Difference (MD)

Peak Signal to Noise Ratio (PSNR) is defined as

PSNR = 10 log10

1

0

21

0

1

0

1

0

2

)()(

255

m

i

n

j

m

i

n

j

jiIjiI (41)

Where I(i j) and I‟(i j) are the corresponding cover image and Stego image pixel

intensities

The Mean square error (MSE) is the measure of average of the square of the errors that is

the difference between the expected value and the actual value

MSE = 1

MNsum sum I(i j) minus Iprime(i j)

Nminus1

0

Mminus1

0

(42)

30

The Normalized Cross Correlation is a measure of similarity of two series as a function

of the lag of one relative to the other

NCC = --------------------------------------------------(43)

Where f(xy) is a original image t(xy) is a reconstructed image 119891 is original image mean

and 119905 is the reconstructed image mean and 120590119891 and 120590119905 is the standard deviation of original

and reconstructed image Absolute difference is measure of finding difference between

original image and the reconstructed image in pixel by pixel manner

AD = -----------------------------------------------------(44)

Where M and N are dimension of row and column respectively

Maximum Difference is the measure of maximum of difference between original and

recovered image

MD = max(original image ndash recovered image) (45)

Let us take F to be original image and be the recovered image

The Structural Content is used for measuring the similarity between two images

(46)

The Normalized Absolute Error is quantity used to measure how close forecasts or

predictions are to the eventual outcomes

(47)

31

The Laplacian Mean Square Error performs well in discriminating the images with

different quality

(48)

where

Figure 41 Gray Scale Cover Images of size 256times256 (a) Barbara (b) Boat


32

Figure 42 Input Image and 2D Logistic Encrypted Image

CTTGGCGAGCAAGATGGCATCTTAGGTTGGCTGAGTCTGCGACCCTCGCTGCGAACGAATCTCC

CTTACTGCGAATGACTGAATCTCGCTGCCGTCGAAAGACCCGTGGAGCCTGTCTCTGAATGCTTG

AGAGCGCACCTACCCACTAAGAAACGAAGCTATACATGCATCGAGTGACGGAATGACAAACTAAT

GAATTAAGTCAGCGAGCTAGCAACTTCTCACGTCCTGTGCCGCGGTCTAAACAAAGAAATAAATA

TATCGAGTTACTGACGTACCTACCCACGAACCTACCTACGAACTAACTCACGAACCGACGAAGAA

ATGTGCCCGATAGAGAGAACCTCTCTGTCTCCCTGAGAAGGACCCTGTCTTCGACGCTAGGAAC

GAGACCTCCCGTACGTAGTACACTGGCCTATCTTGGTTGCGTGTCGTCGGATGATTCTTCGAGGG

AAACTTCCTATGAAGCTGTGAGTCTAGCTCGGATCGCTTGACGCTTGGCAGCTCAGCCTGACCCG

TTGACTCGAGCGGAGAATCTTGGACCGACAGCGGCTTAGATACGCCCTCTCTGCCGAGCTCAGA

GAGACTCGTGGAAAGACGGAACGACTGACGGATCGAGTCTTTGACGGATCGAGGGCGCCTCTGA

AACTCCCTGCCTATCTAACTCAGCAAGACTCTTCCTCTGCAACTGGCTTCCTTACTGAGAAACTCT

CTCACTGACGTTCGGGGAACCTCCGAATCCGGCCTACGTTCTTACTTCCGGTCGTGCGTCATCAA

TCCCCATTAATTGGGGGATGAATCTCCGATACGTTCGGCCTCCCGATGAGAGAAACGGGCGTTCG

CGCCACCCGCCGCTCGTTCTATGATTCTAACTAGCGCAGCTACTTTCTCTCTACCTCAGAGGCTTG

CTCACTCCCGCCCGAGCCGTCGACCCTCCCTCCGTGCGAACTCTCTAGCGCGCCCTCGTTCGTC

CGTCCGGACTGGCTCTGAATCGTGCTGCCGCGGAAACGATCTATCGCCCCCTCTGCCTTGGAAG

GGAGCGCGGGAAGGTAGACCGCCGGGCAGCATCTCACTTCGAGAGCCAGAAACGCTGACAGAA

Figure 43 DNA Sequence

33

Figure 44 Recovered Image

Figure 45 Recovered Text

Table 41Performance Metric Calculation

Image

PSNR MSE AD LMSE NAE MD NCC SC

Barbara 4572 00174 -01054 00076 00064 233 09248 08257

Boat

4491 00209 -00898 00001 00054 230 08161 09811

Butterfly 4584 00163 -01079 00002 00061 207 09702 07058

Charlie

Chaplin

4780 00107 -04982 00001 00117 246 09432 08709

Lena 4724 00122 -03137 00009 00081 218 09595 08570

34

Various attacks have been applied on the encrypted image The performance

metrics of the proposed method have been evaluated between the original and attack

based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)

between the original and the recovered image Bit Error Rate (BER) is calculated

between original and recovered text

S No Attacks on Barbara Image NCC BER

1 Shearing 09043 00057

2 Image Scaling 09037 00043

3 Image Rotating 09031 00047

4 Image color reduction 09046 00051

5 Image blurred 09006 00035

6 Image flip 09069 00044

7 cropping and intensity transformation 09099 00046

8 Image sharpening 09071 00039

9 Gaussian Noise and filtering 09040 00053

10 Image Contrast 09070 00055

11 Speckle Noise and Filtering 09068 00048

Table 42Performance Metric Calculation between original and recovered Barbara

image

Inference

1 As the NCC values are greater than 090 for all types of attacks the proposed

algorithm is reversible

2 As the BER is less than 0006 the proposed algorithm is robust against various

attacks

35

CHAPTER 5

CONCLUSION AND FUTUREWORK

51 CONCLUSION

In this proposed work the 2D-logistic encryption algorithm is used for encrypting the

image and RSA based chaos encryption is used to encrypt the data This proposed scheme

ensures the data security with higher success rates and provides high data embedding

capacity This method provides high security for data that is embedded in the cover image

The cover image is 2D logistic encrypted to embed the data into the cover image to get

better results The image is converted into DNA Sequence to provide additional level of

security Attacks have been applied to the resultant image Peak Signal to Noise Ratio

(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum

Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have

been used to measure the quality of the extracted image The Normalized Cross

Correlation has been calculated between original and recovered image As the NCC values

are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error

Rate is calculated between the original and the recovered text As the BER is less than

0006 the proposed algorithm is robust against various attacks

52 FUTURE WORK

This project can be extended for colour images Embedding performance in spatial

domain can be extended to frequency domain Multiple keys are required for the entire

process and their transfer between sender and receiver requires a secure key exchange

protocol These will be the focus on the future work

36

REFERENCES

1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data

Hiding Algorithm‟ International Journal on New Computer Architectures and

Their Applications (IJNCAA) Vol21 pp 183-192

2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial

problem‟ Science Vol266 pp 1021-1024

3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟

Biotechnology Progress Vol20 pp1605-1607

4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature

Extraction for Biometric Watermarking‟ International Conference on Image

Information Processing (ICIIP 2011)

5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY

USING DNA SEQUENCE‟ Asian Journal Of Computer Science And

information Technology Vol12 pp 50-52

6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image

Steganography Using DNA Sequence‟ International Journal of Engineering

Research and Development Vol217 pp 69-72

7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography

Using DNA Sequence and Sudoku Solution Matrix‟ International journal of

Advanced Research in Computer Science and Software EngineeringVol 22

8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for

Deoxyribonucleic Acid Medium‟International Journal of Innovative

Computing Information and Control Vol35 pp1-16

9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟

Nature Vol399 pp 533-534

10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic

Map‟ proceedings of International Conference on Electronics and

Communication Systems (ICECS-2014) pp 149-153

37

11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟

IEEE International Conference On Green Computing Communication And

Electrical Engineering (ICGCCEE‟14)

12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The

8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-

inspired Optimization Algonthms and Their Applications Track

13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based

Multi-Secret Image Sharing‟ International Conference on Information and

Communication Technologies

14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA

binary strands‟ BioSystems Vol57 pp 13-22

15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding

Based on Contrast Mapping Using DNA Medium‟ The International Arab

Journal of Information Technology Vol82 pp147-154

16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn

improved DNA based dual cover steganography‟proceeding of international

conference on information and communication technologies

17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of

the 5th International Workshop in Information Hiding LNCS Vol 2578pp

373-386

18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based

upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208

19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF

(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-

52

20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data

hiding in encrypted images‟ School of Information Science and Technology

38

LIST OF PUBLICATIONS

1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using

Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and

Security (ICCS-2016) in Pondicherry Engineering College

2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based

Reversible Data Hiding rdquo IEEE Sponsored 3rd

International Conference on Innovation in

Information Embedded and Communication Systems in Karpagam College of

Engineering

BONAFIDE CERTIFICATE

Certified that this project report titled ldquoREVERSIBLE DATA HIDING USING

CHAOTIC AND 2D LOGISTIC ENCRYPTIONrdquo is the bonafide work of

DHASHARATHIR [Reg No 14MCO008] who carried out the research under my

supervision Certified further that to the best of my knowledge the work reported herein

does not form part of any other project or dissertation on the basis of which a degree or

award was conferred on an earlier occasion on this or any other candidate

SIGNATURE SIGNATURE DrAAmsaveni Dr AVasuki

PROJECT SUPERVISOR HEAD OF THE DEPARTMENT Department of ECE Department of ECE Kumaraguru College of Technology Kumaraguru College of Technology COIMBATORE - 641049 COIMBATORE -641049

The Candidate with university Register No 14MCO008 was examined by us in

the project viva ndashvoice examination held on

INTERNAL EXAMINER EXTERNAL EXAMINER

ii

ACKNOWLEDGEMENT



manner






thoughts















iii

ABSTRACT



























iv

TABLE OF CONTENTS


ABSTRACT iv

LIST OF FIGURES vii

LIST OF TABLES viii


1 INTRODUCTION 1

11Cryptography 1

12 Steganography 3







322 Encryption 16

323 Decryption 16


34 DNA Sequence 18

35 Attacks 19

351 Shearing 20


353 Rotation 21


355 Blur Image 22




359 Sharpening 25







51 Conclusion 35

52 Future Work 35

REFERENCES 36


vi

LIST OF TABLES

PAGE NO




2D 2 Dimensional


BER Bit Error Rate








viii

TABLE TITLE

NO

2

vii

LIST OF FIGURES

FIGURE

NO

CAPTION

PAGE

NO







33 Scaling Image 20



36 Blur Image 22

37 Flipped Image 23

38 Cropped Image 24









31


43 DNA Sequence 32



2

1

CHAPTER 1

INTRODUCTION







1) Cryptography

2) Steganography


11 CRYPTOGRAPHY











side


2















decryption


3


There are 2 types





12 STEGANOGRAPHY






















4














irreversible

5














6

CHAPTER 2

LITERATURE SURVEY
















Advantages




7



















Advantages



8
















Advantages

1Security is better


9















Advantages



10














Advantages




11












Advantages



chaos technique

12




No6





















Advantages



13



Technology



















applications

Advantages



image decryption

14

CHAPTER 3











SECRET DATA

COVER IMAGE

CHAOTIC

ENCRYPTION

ENCRY 2D LOGISTIC

ENCRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

CHAOTIC

DECRYPTION

ENCRY

SECRET DATA


DECRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

15



























and decryption

16

321 Key Generation









d= e-1

mod ( (p-1) (q-1))




322 Encryption




Cequiv me

(mod n)

323 Decryption


M equiv cd

(mod n)

17

Algorithm

























18


119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2









Algorithm






shuffling is done)

34 DNA Sequence









19





35 Attacks












1)Shearing

2) Image Scaling

3) Image Rotating


5) Image blurred

6) Image flip


8) Image sharpening


10) Image Contrast


20

351 Shearing



For example

Scale factor 09

Theta10


352 Image Scaling






For example

Scale Factor07

Theta30

scaling Image

21


353 Rotation






For Example

Theta180






Rotated image

22

colors

For Example



355 Blur Image





Fig 36 Blur Image

Color reduced image

50 100 150 200 250 300 350 400 450 500

50

100

150

200

250

300

350

400

450

500

Blurred image

23

356 Flipped Image



357 Cropped Image










Flipped image

24







Cropped Image


25

359 Sharpening
















Sharpened Image

26







Gaussian Noise

Median Filtering

Contrast Image

27











0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000


0 50 100 150 200 250

Speckle Noise

Median Filtering

28



performed


threshold of 2










29

CHAPTER 4













PSNR = 10 log10

1

0

21

0

1

0

1

0

2

)()(

255

m

i

n

j

m

i

n

j

jiIjiI (41)


intensities



MSE = 1


Nminus1

0

Mminus1

0

(42)

30



NCC = --------------------------------------------------(43)





AD = -----------------------------------------------------(44)



recovered image




(46)



(47)

31


different quality

(48)

where



32




















33




Image


Barbara 4572 00174 -01054 00076 00064 233 09248 08257

Boat

4491 00209 -00898 00001 00054 230 08161 09811

Butterfly 4584 00163 -01079 00002 00061 207 09702 07058

Charlie

Chaplin

4780 00107 -04982 00001 00117 246 09432 08709

Lena 4724 00122 -03137 00009 00081 218 09595 08570

34







1 Shearing 09043 00057





6 Image flip 09069 00044







image

Inference




attacks

35

CHAPTER 5


51 CONCLUSION















52 FUTURE WORK





36

REFERENCES




























37




















373-386





52



38









Engineering

ACKNOWLEDGEMENT



manner






thoughts















iii

ABSTRACT



























iv

TABLE OF CONTENTS


ABSTRACT iv

LIST OF FIGURES vii

LIST OF TABLES viii


1 INTRODUCTION 1

11Cryptography 1

12 Steganography 3







322 Encryption 16

323 Decryption 16


34 DNA Sequence 18

35 Attacks 19

351 Shearing 20


353 Rotation 21


355 Blur Image 22




359 Sharpening 25







51 Conclusion 35

52 Future Work 35

REFERENCES 36


vi

LIST OF TABLES

PAGE NO




2D 2 Dimensional


BER Bit Error Rate








viii

TABLE TITLE

NO

2

vii

LIST OF FIGURES

FIGURE

NO

CAPTION

PAGE

NO







33 Scaling Image 20



36 Blur Image 22

37 Flipped Image 23

38 Cropped Image 24









31


43 DNA Sequence 32



2

1

CHAPTER 1

INTRODUCTION







1) Cryptography

2) Steganography


11 CRYPTOGRAPHY











side


2















decryption


3


There are 2 types





12 STEGANOGRAPHY






















4














irreversible

5














6

CHAPTER 2

LITERATURE SURVEY
















Advantages




7



















Advantages



8
















Advantages

1Security is better


9















Advantages



10














Advantages




11












Advantages



chaos technique

12




No6





















Advantages



13



Technology



















applications

Advantages



image decryption

14

CHAPTER 3











SECRET DATA

COVER IMAGE

CHAOTIC

ENCRYPTION

ENCRY 2D LOGISTIC

ENCRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

CHAOTIC

DECRYPTION

ENCRY

SECRET DATA


DECRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

15



























and decryption

16

321 Key Generation









d= e-1

mod ( (p-1) (q-1))




322 Encryption




Cequiv me

(mod n)

323 Decryption


M equiv cd

(mod n)

17

Algorithm

























18


119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2









Algorithm






shuffling is done)

34 DNA Sequence









19





35 Attacks












1)Shearing

2) Image Scaling

3) Image Rotating


5) Image blurred

6) Image flip


8) Image sharpening


10) Image Contrast


20

351 Shearing



For example

Scale factor 09

Theta10


352 Image Scaling






For example

Scale Factor07

Theta30

scaling Image

21


353 Rotation






For Example

Theta180






Rotated image

22

colors

For Example



355 Blur Image





Fig 36 Blur Image

Color reduced image

50 100 150 200 250 300 350 400 450 500

50

100

150

200

250

300

350

400

450

500

Blurred image

23

356 Flipped Image



357 Cropped Image










Flipped image

24







Cropped Image


25

359 Sharpening
















Sharpened Image

26







Gaussian Noise

Median Filtering

Contrast Image

27











0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000


0 50 100 150 200 250

Speckle Noise

Median Filtering

28



performed


threshold of 2










29

CHAPTER 4













PSNR = 10 log10

1

0

21

0

1

0

1

0

2

)()(

255

m

i

n

j

m

i

n

j

jiIjiI (41)


intensities



MSE = 1


Nminus1

0

Mminus1

0

(42)

30



NCC = --------------------------------------------------(43)





AD = -----------------------------------------------------(44)



recovered image




(46)



(47)

31


different quality

(48)

where



32




















33




Image


Barbara 4572 00174 -01054 00076 00064 233 09248 08257

Boat

4491 00209 -00898 00001 00054 230 08161 09811

Butterfly 4584 00163 -01079 00002 00061 207 09702 07058

Charlie

Chaplin

4780 00107 -04982 00001 00117 246 09432 08709

Lena 4724 00122 -03137 00009 00081 218 09595 08570

34







1 Shearing 09043 00057





6 Image flip 09069 00044







image

Inference




attacks

35

CHAPTER 5


51 CONCLUSION















52 FUTURE WORK





36

REFERENCES




























37




















373-386





52



38









Engineering

ABSTRACT



























iv

TABLE OF CONTENTS


ABSTRACT iv

LIST OF FIGURES vii

LIST OF TABLES viii


1 INTRODUCTION 1

11Cryptography 1

12 Steganography 3







322 Encryption 16

323 Decryption 16


34 DNA Sequence 18

35 Attacks 19

351 Shearing 20


353 Rotation 21


355 Blur Image 22




359 Sharpening 25







51 Conclusion 35

52 Future Work 35

REFERENCES 36


vi

LIST OF TABLES

PAGE NO




2D 2 Dimensional


BER Bit Error Rate








viii

TABLE TITLE

NO

2

vii

LIST OF FIGURES

FIGURE

NO

CAPTION

PAGE

NO







33 Scaling Image 20



36 Blur Image 22

37 Flipped Image 23

38 Cropped Image 24









31


43 DNA Sequence 32



2

1

CHAPTER 1

INTRODUCTION







1) Cryptography

2) Steganography


11 CRYPTOGRAPHY











side


2















decryption


3


There are 2 types





12 STEGANOGRAPHY






















4














irreversible

5














6

CHAPTER 2

LITERATURE SURVEY
















Advantages




7



















Advantages



8
















Advantages

1Security is better


9















Advantages



10














Advantages




11












Advantages



chaos technique

12




No6





















Advantages



13



Technology



















applications

Advantages



image decryption

14

CHAPTER 3











SECRET DATA

COVER IMAGE

CHAOTIC

ENCRYPTION

ENCRY 2D LOGISTIC

ENCRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

CHAOTIC

DECRYPTION

ENCRY

SECRET DATA


DECRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

15



























and decryption

16

321 Key Generation









d= e-1

mod ( (p-1) (q-1))




322 Encryption




Cequiv me

(mod n)

323 Decryption


M equiv cd

(mod n)

17

Algorithm

























18


119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2









Algorithm






shuffling is done)

34 DNA Sequence









19





35 Attacks












1)Shearing

2) Image Scaling

3) Image Rotating


5) Image blurred

6) Image flip


8) Image sharpening


10) Image Contrast


20

351 Shearing



For example

Scale factor 09

Theta10


352 Image Scaling






For example

Scale Factor07

Theta30

scaling Image

21


353 Rotation






For Example

Theta180






Rotated image

22

colors

For Example



355 Blur Image





Fig 36 Blur Image

Color reduced image

50 100 150 200 250 300 350 400 450 500

50

100

150

200

250

300

350

400

450

500

Blurred image

23

356 Flipped Image



357 Cropped Image










Flipped image

24







Cropped Image


25

359 Sharpening
















Sharpened Image

26







Gaussian Noise

Median Filtering

Contrast Image

27











0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000


0 50 100 150 200 250

Speckle Noise

Median Filtering

28



performed


threshold of 2










29

CHAPTER 4













PSNR = 10 log10

1

0

21

0

1

0

1

0

2

)()(

255

m

i

n

j

m

i

n

j

jiIjiI (41)


intensities



MSE = 1


Nminus1

0

Mminus1

0

(42)

30



NCC = --------------------------------------------------(43)





AD = -----------------------------------------------------(44)



recovered image




(46)



(47)

31


different quality

(48)

where



32




















33




Image


Barbara 4572 00174 -01054 00076 00064 233 09248 08257

Boat

4491 00209 -00898 00001 00054 230 08161 09811

Butterfly 4584 00163 -01079 00002 00061 207 09702 07058

Charlie

Chaplin

4780 00107 -04982 00001 00117 246 09432 08709

Lena 4724 00122 -03137 00009 00081 218 09595 08570

34







1 Shearing 09043 00057





6 Image flip 09069 00044







image

Inference




attacks

35

CHAPTER 5


51 CONCLUSION















52 FUTURE WORK





36

REFERENCES




























37




















373-386





52



38









Engineering

TABLE OF CONTENTS


ABSTRACT iv

LIST OF FIGURES vii

LIST OF TABLES viii


1 INTRODUCTION 1

11Cryptography 1

12 Steganography 3







322 Encryption 16

323 Decryption 16


34 DNA Sequence 18

35 Attacks 19

351 Shearing 20


353 Rotation 21


355 Blur Image 22




359 Sharpening 25







51 Conclusion 35

52 Future Work 35

REFERENCES 36


vi

LIST OF TABLES

PAGE NO




2D 2 Dimensional


BER Bit Error Rate








viii

TABLE TITLE

NO

2

vii

LIST OF FIGURES

FIGURE

NO

CAPTION

PAGE

NO







33 Scaling Image 20



36 Blur Image 22

37 Flipped Image 23

38 Cropped Image 24









31


43 DNA Sequence 32



2

1

CHAPTER 1

INTRODUCTION







1) Cryptography

2) Steganography


11 CRYPTOGRAPHY











side


2















decryption


3


There are 2 types





12 STEGANOGRAPHY






















4














irreversible

5














6

CHAPTER 2

LITERATURE SURVEY
















Advantages




7



















Advantages



8
















Advantages

1Security is better


9















Advantages



10














Advantages




11












Advantages



chaos technique

12




No6





















Advantages



13



Technology



















applications

Advantages



image decryption

14

CHAPTER 3











SECRET DATA

COVER IMAGE

CHAOTIC

ENCRYPTION

ENCRY 2D LOGISTIC

ENCRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

CHAOTIC

DECRYPTION

ENCRY

SECRET DATA


DECRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

15



























and decryption

16

321 Key Generation









d= e-1

mod ( (p-1) (q-1))




322 Encryption




Cequiv me

(mod n)

323 Decryption


M equiv cd

(mod n)

17

Algorithm

























18


119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2









Algorithm






shuffling is done)

34 DNA Sequence









19





35 Attacks












1)Shearing

2) Image Scaling

3) Image Rotating


5) Image blurred

6) Image flip


8) Image sharpening


10) Image Contrast


20

351 Shearing



For example

Scale factor 09

Theta10


352 Image Scaling






For example

Scale Factor07

Theta30

scaling Image

21


353 Rotation






For Example

Theta180






Rotated image

22

colors

For Example



355 Blur Image





Fig 36 Blur Image

Color reduced image

50 100 150 200 250 300 350 400 450 500

50

100

150

200

250

300

350

400

450

500

Blurred image

23

356 Flipped Image



357 Cropped Image










Flipped image

24







Cropped Image


25

359 Sharpening
















Sharpened Image

26







Gaussian Noise

Median Filtering

Contrast Image

27











0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000


0 50 100 150 200 250

Speckle Noise

Median Filtering

28



performed


threshold of 2










29

CHAPTER 4













PSNR = 10 log10

1

0

21

0

1

0

1

0

2

)()(

255

m

i

n

j

m

i

n

j

jiIjiI (41)


intensities



MSE = 1


Nminus1

0

Mminus1

0

(42)

30



NCC = --------------------------------------------------(43)





AD = -----------------------------------------------------(44)



recovered image




(46)



(47)

31


different quality

(48)

where



32




















33




Image


Barbara 4572 00174 -01054 00076 00064 233 09248 08257

Boat

4491 00209 -00898 00001 00054 230 08161 09811

Butterfly 4584 00163 -01079 00002 00061 207 09702 07058

Charlie

Chaplin

4780 00107 -04982 00001 00117 246 09432 08709

Lena 4724 00122 -03137 00009 00081 218 09595 08570

34







1 Shearing 09043 00057





6 Image flip 09069 00044







image

Inference




attacks

35

CHAPTER 5


51 CONCLUSION















52 FUTURE WORK





36

REFERENCES




























37




















373-386





52



38









Engineering



359 Sharpening 25







51 Conclusion 35

52 Future Work 35

REFERENCES 36


vi

LIST OF TABLES

PAGE NO




2D 2 Dimensional


BER Bit Error Rate








viii

TABLE TITLE

NO

2

vii

LIST OF FIGURES

FIGURE

NO

CAPTION

PAGE

NO







33 Scaling Image 20



36 Blur Image 22

37 Flipped Image 23

38 Cropped Image 24









31


43 DNA Sequence 32



2

1

CHAPTER 1

INTRODUCTION







1) Cryptography

2) Steganography


11 CRYPTOGRAPHY











side


2















decryption


3


There are 2 types





12 STEGANOGRAPHY






















4














irreversible

5














6

CHAPTER 2

LITERATURE SURVEY
















Advantages




7



















Advantages



8
















Advantages

1Security is better


9















Advantages



10














Advantages




11












Advantages



chaos technique

12




No6





















Advantages



13



Technology



















applications

Advantages



image decryption

14

CHAPTER 3











SECRET DATA

COVER IMAGE

CHAOTIC

ENCRYPTION

ENCRY 2D LOGISTIC

ENCRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

CHAOTIC

DECRYPTION

ENCRY

SECRET DATA


DECRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

15



























and decryption

16

321 Key Generation









d= e-1

mod ( (p-1) (q-1))




322 Encryption




Cequiv me

(mod n)

323 Decryption


M equiv cd

(mod n)

17

Algorithm

























18


119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2









Algorithm






shuffling is done)

34 DNA Sequence









19





35 Attacks












1)Shearing

2) Image Scaling

3) Image Rotating


5) Image blurred

6) Image flip


8) Image sharpening


10) Image Contrast


20

351 Shearing



For example

Scale factor 09

Theta10


352 Image Scaling






For example

Scale Factor07

Theta30

scaling Image

21


353 Rotation






For Example

Theta180






Rotated image

22

colors

For Example



355 Blur Image





Fig 36 Blur Image

Color reduced image

50 100 150 200 250 300 350 400 450 500

50

100

150

200

250

300

350

400

450

500

Blurred image

23

356 Flipped Image



357 Cropped Image










Flipped image

24







Cropped Image


25

359 Sharpening
















Sharpened Image

26







Gaussian Noise

Median Filtering

Contrast Image

27











0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000


0 50 100 150 200 250

Speckle Noise

Median Filtering

28



performed


threshold of 2










29

CHAPTER 4













PSNR = 10 log10

1

0

21

0

1

0

1

0

2

)()(

255

m

i

n

j

m

i

n

j

jiIjiI (41)


intensities



MSE = 1


Nminus1

0

Mminus1

0

(42)

30



NCC = --------------------------------------------------(43)





AD = -----------------------------------------------------(44)



recovered image




(46)



(47)

31


different quality

(48)

where



32




















33




Image


Barbara 4572 00174 -01054 00076 00064 233 09248 08257

Boat

4491 00209 -00898 00001 00054 230 08161 09811

Butterfly 4584 00163 -01079 00002 00061 207 09702 07058

Charlie

Chaplin

4780 00107 -04982 00001 00117 246 09432 08709

Lena 4724 00122 -03137 00009 00081 218 09595 08570

34







1 Shearing 09043 00057





6 Image flip 09069 00044







image

Inference




attacks

35

CHAPTER 5


51 CONCLUSION















52 FUTURE WORK





36

REFERENCES




























37




















373-386





52



38









Engineering

LIST OF TABLES

PAGE NO




2D 2 Dimensional


BER Bit Error Rate








viii

TABLE TITLE

NO

2

vii

LIST OF FIGURES

FIGURE

NO

CAPTION

PAGE

NO







33 Scaling Image 20



36 Blur Image 22

37 Flipped Image 23

38 Cropped Image 24









31


43 DNA Sequence 32



2

1

CHAPTER 1

INTRODUCTION







1) Cryptography

2) Steganography


11 CRYPTOGRAPHY











side


2















decryption


3


There are 2 types





12 STEGANOGRAPHY






















4














irreversible

5














6

CHAPTER 2

LITERATURE SURVEY
















Advantages




7



















Advantages



8
















Advantages

1Security is better


9















Advantages



10














Advantages




11












Advantages



chaos technique

12




No6





















Advantages



13



Technology



















applications

Advantages



image decryption

14

CHAPTER 3











SECRET DATA

COVER IMAGE

CHAOTIC

ENCRYPTION

ENCRY 2D LOGISTIC

ENCRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

CHAOTIC

DECRYPTION

ENCRY

SECRET DATA


DECRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

15



























and decryption

16

321 Key Generation









d= e-1

mod ( (p-1) (q-1))




322 Encryption




Cequiv me

(mod n)

323 Decryption


M equiv cd

(mod n)

17

Algorithm

























18


119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2









Algorithm






shuffling is done)

34 DNA Sequence









19





35 Attacks












1)Shearing

2) Image Scaling

3) Image Rotating


5) Image blurred

6) Image flip


8) Image sharpening


10) Image Contrast


20

351 Shearing



For example

Scale factor 09

Theta10


352 Image Scaling






For example

Scale Factor07

Theta30

scaling Image

21


353 Rotation






For Example

Theta180






Rotated image

22

colors

For Example



355 Blur Image





Fig 36 Blur Image

Color reduced image

50 100 150 200 250 300 350 400 450 500

50

100

150

200

250

300

350

400

450

500

Blurred image

23

356 Flipped Image



357 Cropped Image










Flipped image

24







Cropped Image


25

359 Sharpening
















Sharpened Image

26







Gaussian Noise

Median Filtering

Contrast Image

27











0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000


0 50 100 150 200 250

Speckle Noise

Median Filtering

28



performed


threshold of 2










29

CHAPTER 4













PSNR = 10 log10

1

0

21

0

1

0

1

0

2

)()(

255

m

i

n

j

m

i

n

j

jiIjiI (41)


intensities



MSE = 1


Nminus1

0

Mminus1

0

(42)

30



NCC = --------------------------------------------------(43)





AD = -----------------------------------------------------(44)



recovered image




(46)



(47)

31


different quality

(48)

where



32




















33




Image


Barbara 4572 00174 -01054 00076 00064 233 09248 08257

Boat

4491 00209 -00898 00001 00054 230 08161 09811

Butterfly 4584 00163 -01079 00002 00061 207 09702 07058

Charlie

Chaplin

4780 00107 -04982 00001 00117 246 09432 08709

Lena 4724 00122 -03137 00009 00081 218 09595 08570

34







1 Shearing 09043 00057





6 Image flip 09069 00044







image

Inference




attacks

35

CHAPTER 5


51 CONCLUSION















52 FUTURE WORK





36

REFERENCES




























37




















373-386





52



38









Engineering

2

vii

LIST OF FIGURES

FIGURE

NO

CAPTION

PAGE

NO







33 Scaling Image 20



36 Blur Image 22

37 Flipped Image 23

38 Cropped Image 24









31


43 DNA Sequence 32



2

1

CHAPTER 1

INTRODUCTION







1) Cryptography

2) Steganography


11 CRYPTOGRAPHY











side


2















decryption


3


There are 2 types





12 STEGANOGRAPHY






















4














irreversible

5














6

CHAPTER 2

LITERATURE SURVEY
















Advantages




7



















Advantages



8
















Advantages

1Security is better


9















Advantages



10














Advantages




11












Advantages



chaos technique

12




No6





















Advantages



13



Technology



















applications

Advantages



image decryption

14

CHAPTER 3











SECRET DATA

COVER IMAGE

CHAOTIC

ENCRYPTION

ENCRY 2D LOGISTIC

ENCRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

CHAOTIC

DECRYPTION

ENCRY

SECRET DATA


DECRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

15



























and decryption

16

321 Key Generation









d= e-1

mod ( (p-1) (q-1))




322 Encryption




Cequiv me

(mod n)

323 Decryption


M equiv cd

(mod n)

17

Algorithm

























18


119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2









Algorithm






shuffling is done)

34 DNA Sequence









19





35 Attacks












1)Shearing

2) Image Scaling

3) Image Rotating


5) Image blurred

6) Image flip


8) Image sharpening


10) Image Contrast


20

351 Shearing



For example

Scale factor 09

Theta10


352 Image Scaling






For example

Scale Factor07

Theta30

scaling Image

21


353 Rotation






For Example

Theta180






Rotated image

22

colors

For Example



355 Blur Image





Fig 36 Blur Image

Color reduced image

50 100 150 200 250 300 350 400 450 500

50

100

150

200

250

300

350

400

450

500

Blurred image

23

356 Flipped Image



357 Cropped Image










Flipped image

24







Cropped Image


25

359 Sharpening
















Sharpened Image

26







Gaussian Noise

Median Filtering

Contrast Image

27











0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000


0 50 100 150 200 250

Speckle Noise

Median Filtering

28



performed


threshold of 2










29

CHAPTER 4













PSNR = 10 log10

1

0

21

0

1

0

1

0

2

)()(

255

m

i

n

j

m

i

n

j

jiIjiI (41)


intensities



MSE = 1


Nminus1

0

Mminus1

0

(42)

30



NCC = --------------------------------------------------(43)





AD = -----------------------------------------------------(44)



recovered image




(46)



(47)

31


different quality

(48)

where



32




















33




Image


Barbara 4572 00174 -01054 00076 00064 233 09248 08257

Boat

4491 00209 -00898 00001 00054 230 08161 09811

Butterfly 4584 00163 -01079 00002 00061 207 09702 07058

Charlie

Chaplin

4780 00107 -04982 00001 00117 246 09432 08709

Lena 4724 00122 -03137 00009 00081 218 09595 08570

34







1 Shearing 09043 00057





6 Image flip 09069 00044







image

Inference




attacks

35

CHAPTER 5


51 CONCLUSION















52 FUTURE WORK





36

REFERENCES




























37




















373-386





52



38









Engineering

vii

LIST OF FIGURES

FIGURE

NO

CAPTION

PAGE

NO







33 Scaling Image 20



36 Blur Image 22

37 Flipped Image 23

38 Cropped Image 24









31


43 DNA Sequence 32



2

1

CHAPTER 1

INTRODUCTION







1) Cryptography

2) Steganography


11 CRYPTOGRAPHY











side


2















decryption


3


There are 2 types





12 STEGANOGRAPHY






















4














irreversible

5














6

CHAPTER 2

LITERATURE SURVEY
















Advantages




7



















Advantages



8
















Advantages

1Security is better


9















Advantages



10














Advantages




11












Advantages



chaos technique

12




No6





















Advantages



13



Technology



















applications

Advantages



image decryption

14

CHAPTER 3











SECRET DATA

COVER IMAGE

CHAOTIC

ENCRYPTION

ENCRY 2D LOGISTIC

ENCRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

CHAOTIC

DECRYPTION

ENCRY

SECRET DATA


DECRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

15



























and decryption

16

321 Key Generation









d= e-1

mod ( (p-1) (q-1))




322 Encryption




Cequiv me

(mod n)

323 Decryption


M equiv cd

(mod n)

17

Algorithm

























18


119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2









Algorithm






shuffling is done)

34 DNA Sequence









19





35 Attacks












1)Shearing

2) Image Scaling

3) Image Rotating


5) Image blurred

6) Image flip


8) Image sharpening


10) Image Contrast


20

351 Shearing



For example

Scale factor 09

Theta10


352 Image Scaling






For example

Scale Factor07

Theta30

scaling Image

21


353 Rotation






For Example

Theta180






Rotated image

22

colors

For Example



355 Blur Image





Fig 36 Blur Image

Color reduced image

50 100 150 200 250 300 350 400 450 500

50

100

150

200

250

300

350

400

450

500

Blurred image

23

356 Flipped Image



357 Cropped Image










Flipped image

24







Cropped Image


25

359 Sharpening
















Sharpened Image

26







Gaussian Noise

Median Filtering

Contrast Image

27











0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000


0 50 100 150 200 250

Speckle Noise

Median Filtering

28



performed


threshold of 2










29

CHAPTER 4













PSNR = 10 log10

1

0

21

0

1

0

1

0

2

)()(

255

m

i

n

j

m

i

n

j

jiIjiI (41)


intensities



MSE = 1


Nminus1

0

Mminus1

0

(42)

30



NCC = --------------------------------------------------(43)





AD = -----------------------------------------------------(44)



recovered image




(46)



(47)

31


different quality

(48)

where



32




















33




Image


Barbara 4572 00174 -01054 00076 00064 233 09248 08257

Boat

4491 00209 -00898 00001 00054 230 08161 09811

Butterfly 4584 00163 -01079 00002 00061 207 09702 07058

Charlie

Chaplin

4780 00107 -04982 00001 00117 246 09432 08709

Lena 4724 00122 -03137 00009 00081 218 09595 08570

34







1 Shearing 09043 00057





6 Image flip 09069 00044







image

Inference




attacks

35

CHAPTER 5


51 CONCLUSION















52 FUTURE WORK





36

REFERENCES




























37




















373-386





52



38









Engineering

2

1

CHAPTER 1

INTRODUCTION







1) Cryptography

2) Steganography


11 CRYPTOGRAPHY











side


2















decryption


3


There are 2 types





12 STEGANOGRAPHY






















4














irreversible

5














6

CHAPTER 2

LITERATURE SURVEY
















Advantages




7



















Advantages



8
















Advantages

1Security is better


9















Advantages



10














Advantages




11












Advantages



chaos technique

12




No6





















Advantages



13



Technology



















applications

Advantages



image decryption

14

CHAPTER 3











SECRET DATA

COVER IMAGE

CHAOTIC

ENCRYPTION

ENCRY 2D LOGISTIC

ENCRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

CHAOTIC

DECRYPTION

ENCRY

SECRET DATA


DECRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

15



























and decryption

16

321 Key Generation









d= e-1

mod ( (p-1) (q-1))




322 Encryption




Cequiv me

(mod n)

323 Decryption


M equiv cd

(mod n)

17

Algorithm

























18


119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2









Algorithm






shuffling is done)

34 DNA Sequence









19





35 Attacks












1)Shearing

2) Image Scaling

3) Image Rotating


5) Image blurred

6) Image flip


8) Image sharpening


10) Image Contrast


20

351 Shearing



For example

Scale factor 09

Theta10


352 Image Scaling






For example

Scale Factor07

Theta30

scaling Image

21


353 Rotation






For Example

Theta180






Rotated image

22

colors

For Example



355 Blur Image





Fig 36 Blur Image

Color reduced image

50 100 150 200 250 300 350 400 450 500

50

100

150

200

250

300

350

400

450

500

Blurred image

23

356 Flipped Image



357 Cropped Image










Flipped image

24







Cropped Image


25

359 Sharpening
















Sharpened Image

26







Gaussian Noise

Median Filtering

Contrast Image

27











0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000


0 50 100 150 200 250

Speckle Noise

Median Filtering

28



performed


threshold of 2










29

CHAPTER 4













PSNR = 10 log10

1

0

21

0

1

0

1

0

2

)()(

255

m

i

n

j

m

i

n

j

jiIjiI (41)


intensities



MSE = 1


Nminus1

0

Mminus1

0

(42)

30



NCC = --------------------------------------------------(43)





AD = -----------------------------------------------------(44)



recovered image




(46)



(47)

31


different quality

(48)

where



32




















33




Image


Barbara 4572 00174 -01054 00076 00064 233 09248 08257

Boat

4491 00209 -00898 00001 00054 230 08161 09811

Butterfly 4584 00163 -01079 00002 00061 207 09702 07058

Charlie

Chaplin

4780 00107 -04982 00001 00117 246 09432 08709

Lena 4724 00122 -03137 00009 00081 218 09595 08570

34







1 Shearing 09043 00057





6 Image flip 09069 00044







image

Inference




attacks

35

CHAPTER 5


51 CONCLUSION















52 FUTURE WORK





36

REFERENCES




























37




















373-386





52



38









Engineering

1

CHAPTER 1

INTRODUCTION







1) Cryptography

2) Steganography


11 CRYPTOGRAPHY











side


2















decryption


3


There are 2 types





12 STEGANOGRAPHY






















4














irreversible

5














6

CHAPTER 2

LITERATURE SURVEY
















Advantages




7



















Advantages



8
















Advantages

1Security is better


9















Advantages



10














Advantages




11












Advantages



chaos technique

12




No6





















Advantages



13



Technology



















applications

Advantages



image decryption

14

CHAPTER 3











SECRET DATA

COVER IMAGE

CHAOTIC

ENCRYPTION

ENCRY 2D LOGISTIC

ENCRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

CHAOTIC

DECRYPTION

ENCRY

SECRET DATA


DECRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

15



























and decryption

16

321 Key Generation









d= e-1

mod ( (p-1) (q-1))




322 Encryption




Cequiv me

(mod n)

323 Decryption


M equiv cd

(mod n)

17

Algorithm

























18


119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2









Algorithm






shuffling is done)

34 DNA Sequence









19





35 Attacks












1)Shearing

2) Image Scaling

3) Image Rotating


5) Image blurred

6) Image flip


8) Image sharpening


10) Image Contrast


20

351 Shearing



For example

Scale factor 09

Theta10


352 Image Scaling






For example

Scale Factor07

Theta30

scaling Image

21


353 Rotation






For Example

Theta180






Rotated image

22

colors

For Example



355 Blur Image





Fig 36 Blur Image

Color reduced image

50 100 150 200 250 300 350 400 450 500

50

100

150

200

250

300

350

400

450

500

Blurred image

23

356 Flipped Image



357 Cropped Image










Flipped image

24







Cropped Image


25

359 Sharpening
















Sharpened Image

26







Gaussian Noise

Median Filtering

Contrast Image

27











0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000


0 50 100 150 200 250

Speckle Noise

Median Filtering

28



performed


threshold of 2










29

CHAPTER 4













PSNR = 10 log10

1

0

21

0

1

0

1

0

2

)()(

255

m

i

n

j

m

i

n

j

jiIjiI (41)


intensities



MSE = 1


Nminus1

0

Mminus1

0

(42)

30



NCC = --------------------------------------------------(43)





AD = -----------------------------------------------------(44)



recovered image




(46)



(47)

31


different quality

(48)

where



32




















33




Image


Barbara 4572 00174 -01054 00076 00064 233 09248 08257

Boat

4491 00209 -00898 00001 00054 230 08161 09811

Butterfly 4584 00163 -01079 00002 00061 207 09702 07058

Charlie

Chaplin

4780 00107 -04982 00001 00117 246 09432 08709

Lena 4724 00122 -03137 00009 00081 218 09595 08570

34







1 Shearing 09043 00057





6 Image flip 09069 00044







image

Inference




attacks

35

CHAPTER 5


51 CONCLUSION















52 FUTURE WORK





36

REFERENCES




























37




















373-386





52



38









Engineering

2















decryption


3


There are 2 types





12 STEGANOGRAPHY






















4














irreversible

5














6

CHAPTER 2

LITERATURE SURVEY
















Advantages




7



















Advantages



8
















Advantages

1Security is better


9















Advantages



10














Advantages




11












Advantages



chaos technique

12




No6





















Advantages



13



Technology



















applications

Advantages



image decryption

14

CHAPTER 3











SECRET DATA

COVER IMAGE

CHAOTIC

ENCRYPTION

ENCRY 2D LOGISTIC

ENCRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

CHAOTIC

DECRYPTION

ENCRY

SECRET DATA


DECRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

15



























and decryption

16

321 Key Generation









d= e-1

mod ( (p-1) (q-1))




322 Encryption




Cequiv me

(mod n)

323 Decryption


M equiv cd

(mod n)

17

Algorithm

























18


119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2









Algorithm






shuffling is done)

34 DNA Sequence









19





35 Attacks












1)Shearing

2) Image Scaling

3) Image Rotating


5) Image blurred

6) Image flip


8) Image sharpening


10) Image Contrast


20

351 Shearing



For example

Scale factor 09

Theta10


352 Image Scaling






For example

Scale Factor07

Theta30

scaling Image

21


353 Rotation






For Example

Theta180






Rotated image

22

colors

For Example



355 Blur Image





Fig 36 Blur Image

Color reduced image

50 100 150 200 250 300 350 400 450 500

50

100

150

200

250

300

350

400

450

500

Blurred image

23

356 Flipped Image



357 Cropped Image










Flipped image

24







Cropped Image


25

359 Sharpening
















Sharpened Image

26







Gaussian Noise

Median Filtering

Contrast Image

27











0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000


0 50 100 150 200 250

Speckle Noise

Median Filtering

28



performed


threshold of 2










29

CHAPTER 4













PSNR = 10 log10

1

0

21

0

1

0

1

0

2

)()(

255

m

i

n

j

m

i

n

j

jiIjiI (41)


intensities



MSE = 1


Nminus1

0

Mminus1

0

(42)

30



NCC = --------------------------------------------------(43)





AD = -----------------------------------------------------(44)



recovered image




(46)



(47)

31


different quality

(48)

where



32




















33




Image


Barbara 4572 00174 -01054 00076 00064 233 09248 08257

Boat

4491 00209 -00898 00001 00054 230 08161 09811

Butterfly 4584 00163 -01079 00002 00061 207 09702 07058

Charlie

Chaplin

4780 00107 -04982 00001 00117 246 09432 08709

Lena 4724 00122 -03137 00009 00081 218 09595 08570

34







1 Shearing 09043 00057





6 Image flip 09069 00044







image

Inference




attacks

35

CHAPTER 5


51 CONCLUSION















52 FUTURE WORK





36

REFERENCES




























37




















373-386





52



38









Engineering

3


There are 2 types





12 STEGANOGRAPHY






















4














irreversible

5














6

CHAPTER 2

LITERATURE SURVEY
















Advantages




7



















Advantages



8
















Advantages

1Security is better


9















Advantages



10














Advantages




11












Advantages



chaos technique

12




No6





















Advantages



13



Technology



















applications

Advantages



image decryption

14

CHAPTER 3











SECRET DATA

COVER IMAGE

CHAOTIC

ENCRYPTION

ENCRY 2D LOGISTIC

ENCRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

CHAOTIC

DECRYPTION

ENCRY

SECRET DATA


DECRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

15



























and decryption

16

321 Key Generation









d= e-1

mod ( (p-1) (q-1))




322 Encryption




Cequiv me

(mod n)

323 Decryption


M equiv cd

(mod n)

17

Algorithm

























18


119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2









Algorithm






shuffling is done)

34 DNA Sequence









19





35 Attacks












1)Shearing

2) Image Scaling

3) Image Rotating


5) Image blurred

6) Image flip


8) Image sharpening


10) Image Contrast


20

351 Shearing



For example

Scale factor 09

Theta10


352 Image Scaling






For example

Scale Factor07

Theta30

scaling Image

21


353 Rotation






For Example

Theta180






Rotated image

22

colors

For Example



355 Blur Image





Fig 36 Blur Image

Color reduced image

50 100 150 200 250 300 350 400 450 500

50

100

150

200

250

300

350

400

450

500

Blurred image

23

356 Flipped Image



357 Cropped Image










Flipped image

24







Cropped Image


25

359 Sharpening
















Sharpened Image

26







Gaussian Noise

Median Filtering

Contrast Image

27











0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000


0 50 100 150 200 250

Speckle Noise

Median Filtering

28



performed


threshold of 2










29

CHAPTER 4













PSNR = 10 log10

1

0

21

0

1

0

1

0

2

)()(

255

m

i

n

j

m

i

n

j

jiIjiI (41)


intensities



MSE = 1


Nminus1

0

Mminus1

0

(42)

30



NCC = --------------------------------------------------(43)





AD = -----------------------------------------------------(44)



recovered image




(46)



(47)

31


different quality

(48)

where



32




















33




Image


Barbara 4572 00174 -01054 00076 00064 233 09248 08257

Boat

4491 00209 -00898 00001 00054 230 08161 09811

Butterfly 4584 00163 -01079 00002 00061 207 09702 07058

Charlie

Chaplin

4780 00107 -04982 00001 00117 246 09432 08709

Lena 4724 00122 -03137 00009 00081 218 09595 08570

34







1 Shearing 09043 00057





6 Image flip 09069 00044







image

Inference




attacks

35

CHAPTER 5


51 CONCLUSION















52 FUTURE WORK





36

REFERENCES




























37




















373-386





52



38









Engineering

4














irreversible

5














6

CHAPTER 2

LITERATURE SURVEY
















Advantages




7



















Advantages



8
















Advantages

1Security is better


9















Advantages



10














Advantages




11












Advantages



chaos technique

12




No6





















Advantages



13



Technology



















applications

Advantages



image decryption

14

CHAPTER 3











SECRET DATA

COVER IMAGE

CHAOTIC

ENCRYPTION

ENCRY 2D LOGISTIC

ENCRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

CHAOTIC

DECRYPTION

ENCRY

SECRET DATA


DECRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

15



























and decryption

16

321 Key Generation









d= e-1

mod ( (p-1) (q-1))




322 Encryption




Cequiv me

(mod n)

323 Decryption


M equiv cd

(mod n)

17

Algorithm

























18


119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2









Algorithm






shuffling is done)

34 DNA Sequence









19





35 Attacks












1)Shearing

2) Image Scaling

3) Image Rotating


5) Image blurred

6) Image flip


8) Image sharpening


10) Image Contrast


20

351 Shearing



For example

Scale factor 09

Theta10


352 Image Scaling






For example

Scale Factor07

Theta30

scaling Image

21


353 Rotation






For Example

Theta180






Rotated image

22

colors

For Example



355 Blur Image





Fig 36 Blur Image

Color reduced image

50 100 150 200 250 300 350 400 450 500

50

100

150

200

250

300

350

400

450

500

Blurred image

23

356 Flipped Image



357 Cropped Image










Flipped image

24







Cropped Image


25

359 Sharpening
















Sharpened Image

26







Gaussian Noise

Median Filtering

Contrast Image

27











0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000


0 50 100 150 200 250

Speckle Noise

Median Filtering

28



performed


threshold of 2










29

CHAPTER 4













PSNR = 10 log10

1

0

21

0

1

0

1

0

2

)()(

255

m

i

n

j

m

i

n

j

jiIjiI (41)


intensities



MSE = 1


Nminus1

0

Mminus1

0

(42)

30



NCC = --------------------------------------------------(43)





AD = -----------------------------------------------------(44)



recovered image




(46)



(47)

31


different quality

(48)

where



32




















33




Image


Barbara 4572 00174 -01054 00076 00064 233 09248 08257

Boat

4491 00209 -00898 00001 00054 230 08161 09811

Butterfly 4584 00163 -01079 00002 00061 207 09702 07058

Charlie

Chaplin

4780 00107 -04982 00001 00117 246 09432 08709

Lena 4724 00122 -03137 00009 00081 218 09595 08570

34







1 Shearing 09043 00057





6 Image flip 09069 00044







image

Inference




attacks

35

CHAPTER 5


51 CONCLUSION















52 FUTURE WORK





36

REFERENCES




























37




















373-386





52



38









Engineering

5














6

CHAPTER 2

LITERATURE SURVEY
















Advantages




7



















Advantages



8
















Advantages

1Security is better


9















Advantages



10














Advantages




11












Advantages



chaos technique

12




No6





















Advantages



13



Technology



















applications

Advantages



image decryption

14

CHAPTER 3











SECRET DATA

COVER IMAGE

CHAOTIC

ENCRYPTION

ENCRY 2D LOGISTIC

ENCRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

CHAOTIC

DECRYPTION

ENCRY

SECRET DATA


DECRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

15



























and decryption

16

321 Key Generation









d= e-1

mod ( (p-1) (q-1))




322 Encryption




Cequiv me

(mod n)

323 Decryption


M equiv cd

(mod n)

17

Algorithm

























18


119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2









Algorithm






shuffling is done)

34 DNA Sequence









19





35 Attacks












1)Shearing

2) Image Scaling

3) Image Rotating


5) Image blurred

6) Image flip


8) Image sharpening


10) Image Contrast


20

351 Shearing



For example

Scale factor 09

Theta10


352 Image Scaling






For example

Scale Factor07

Theta30

scaling Image

21


353 Rotation






For Example

Theta180






Rotated image

22

colors

For Example



355 Blur Image





Fig 36 Blur Image

Color reduced image

50 100 150 200 250 300 350 400 450 500

50

100

150

200

250

300

350

400

450

500

Blurred image

23

356 Flipped Image



357 Cropped Image










Flipped image

24







Cropped Image


25

359 Sharpening
















Sharpened Image

26







Gaussian Noise

Median Filtering

Contrast Image

27











0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000


0 50 100 150 200 250

Speckle Noise

Median Filtering

28



performed


threshold of 2










29

CHAPTER 4













PSNR = 10 log10

1

0

21

0

1

0

1

0

2

)()(

255

m

i

n

j

m

i

n

j

jiIjiI (41)


intensities



MSE = 1


Nminus1

0

Mminus1

0

(42)

30



NCC = --------------------------------------------------(43)





AD = -----------------------------------------------------(44)



recovered image




(46)



(47)

31


different quality

(48)

where



32




















33




Image


Barbara 4572 00174 -01054 00076 00064 233 09248 08257

Boat

4491 00209 -00898 00001 00054 230 08161 09811

Butterfly 4584 00163 -01079 00002 00061 207 09702 07058

Charlie

Chaplin

4780 00107 -04982 00001 00117 246 09432 08709

Lena 4724 00122 -03137 00009 00081 218 09595 08570

34







1 Shearing 09043 00057





6 Image flip 09069 00044







image

Inference




attacks

35

CHAPTER 5


51 CONCLUSION















52 FUTURE WORK





36

REFERENCES




























37




















373-386





52



38









Engineering

6

CHAPTER 2

LITERATURE SURVEY
















Advantages




7



















Advantages



8
















Advantages

1Security is better


9















Advantages



10














Advantages




11












Advantages



chaos technique

12




No6





















Advantages



13



Technology



















applications

Advantages



image decryption

14

CHAPTER 3











SECRET DATA

COVER IMAGE

CHAOTIC

ENCRYPTION

ENCRY 2D LOGISTIC

ENCRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

CHAOTIC

DECRYPTION

ENCRY

SECRET DATA


DECRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

15



























and decryption

16

321 Key Generation









d= e-1

mod ( (p-1) (q-1))




322 Encryption




Cequiv me

(mod n)

323 Decryption


M equiv cd

(mod n)

17

Algorithm

























18


119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2









Algorithm






shuffling is done)

34 DNA Sequence









19





35 Attacks












1)Shearing

2) Image Scaling

3) Image Rotating


5) Image blurred

6) Image flip


8) Image sharpening


10) Image Contrast


20

351 Shearing



For example

Scale factor 09

Theta10


352 Image Scaling






For example

Scale Factor07

Theta30

scaling Image

21


353 Rotation






For Example

Theta180






Rotated image

22

colors

For Example



355 Blur Image





Fig 36 Blur Image

Color reduced image

50 100 150 200 250 300 350 400 450 500

50

100

150

200

250

300

350

400

450

500

Blurred image

23

356 Flipped Image



357 Cropped Image










Flipped image

24







Cropped Image


25

359 Sharpening
















Sharpened Image

26







Gaussian Noise

Median Filtering

Contrast Image

27











0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000


0 50 100 150 200 250

Speckle Noise

Median Filtering

28



performed


threshold of 2










29

CHAPTER 4













PSNR = 10 log10

1

0

21

0

1

0

1

0

2

)()(

255

m

i

n

j

m

i

n

j

jiIjiI (41)


intensities



MSE = 1


Nminus1

0

Mminus1

0

(42)

30



NCC = --------------------------------------------------(43)





AD = -----------------------------------------------------(44)



recovered image




(46)



(47)

31


different quality

(48)

where



32




















33




Image


Barbara 4572 00174 -01054 00076 00064 233 09248 08257

Boat

4491 00209 -00898 00001 00054 230 08161 09811

Butterfly 4584 00163 -01079 00002 00061 207 09702 07058

Charlie

Chaplin

4780 00107 -04982 00001 00117 246 09432 08709

Lena 4724 00122 -03137 00009 00081 218 09595 08570

34







1 Shearing 09043 00057





6 Image flip 09069 00044







image

Inference




attacks

35

CHAPTER 5


51 CONCLUSION















52 FUTURE WORK





36

REFERENCES




























37




















373-386





52



38









Engineering

7



















Advantages



8
















Advantages

1Security is better


9















Advantages



10














Advantages




11












Advantages



chaos technique

12




No6





















Advantages



13



Technology



















applications

Advantages



image decryption

14

CHAPTER 3











SECRET DATA

COVER IMAGE

CHAOTIC

ENCRYPTION

ENCRY 2D LOGISTIC

ENCRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

CHAOTIC

DECRYPTION

ENCRY

SECRET DATA


DECRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

15



























and decryption

16

321 Key Generation









d= e-1

mod ( (p-1) (q-1))




322 Encryption




Cequiv me

(mod n)

323 Decryption


M equiv cd

(mod n)

17

Algorithm

























18


119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2









Algorithm






shuffling is done)

34 DNA Sequence









19





35 Attacks












1)Shearing

2) Image Scaling

3) Image Rotating


5) Image blurred

6) Image flip


8) Image sharpening


10) Image Contrast


20

351 Shearing



For example

Scale factor 09

Theta10


352 Image Scaling






For example

Scale Factor07

Theta30

scaling Image

21


353 Rotation






For Example

Theta180






Rotated image

22

colors

For Example



355 Blur Image





Fig 36 Blur Image

Color reduced image

50 100 150 200 250 300 350 400 450 500

50

100

150

200

250

300

350

400

450

500

Blurred image

23

356 Flipped Image



357 Cropped Image










Flipped image

24







Cropped Image


25

359 Sharpening
















Sharpened Image

26







Gaussian Noise

Median Filtering

Contrast Image

27











0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000


0 50 100 150 200 250

Speckle Noise

Median Filtering

28



performed


threshold of 2










29

CHAPTER 4













PSNR = 10 log10

1

0

21

0

1

0

1

0

2

)()(

255

m

i

n

j

m

i

n

j

jiIjiI (41)


intensities



MSE = 1


Nminus1

0

Mminus1

0

(42)

30



NCC = --------------------------------------------------(43)





AD = -----------------------------------------------------(44)



recovered image




(46)



(47)

31


different quality

(48)

where



32




















33




Image


Barbara 4572 00174 -01054 00076 00064 233 09248 08257

Boat

4491 00209 -00898 00001 00054 230 08161 09811

Butterfly 4584 00163 -01079 00002 00061 207 09702 07058

Charlie

Chaplin

4780 00107 -04982 00001 00117 246 09432 08709

Lena 4724 00122 -03137 00009 00081 218 09595 08570

34







1 Shearing 09043 00057





6 Image flip 09069 00044







image

Inference




attacks

35

CHAPTER 5


51 CONCLUSION















52 FUTURE WORK





36

REFERENCES




























37




















373-386





52



38









Engineering

8
















Advantages

1Security is better


9















Advantages



10














Advantages




11












Advantages



chaos technique

12




No6





















Advantages



13



Technology



















applications

Advantages



image decryption

14

CHAPTER 3











SECRET DATA

COVER IMAGE

CHAOTIC

ENCRYPTION

ENCRY 2D LOGISTIC

ENCRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

CHAOTIC

DECRYPTION

ENCRY

SECRET DATA


DECRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

15



























and decryption

16

321 Key Generation









d= e-1

mod ( (p-1) (q-1))




322 Encryption




Cequiv me

(mod n)

323 Decryption


M equiv cd

(mod n)

17

Algorithm

























18


119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2









Algorithm






shuffling is done)

34 DNA Sequence









19





35 Attacks












1)Shearing

2) Image Scaling

3) Image Rotating


5) Image blurred

6) Image flip


8) Image sharpening


10) Image Contrast


20

351 Shearing



For example

Scale factor 09

Theta10


352 Image Scaling






For example

Scale Factor07

Theta30

scaling Image

21


353 Rotation






For Example

Theta180






Rotated image

22

colors

For Example



355 Blur Image





Fig 36 Blur Image

Color reduced image

50 100 150 200 250 300 350 400 450 500

50

100

150

200

250

300

350

400

450

500

Blurred image

23

356 Flipped Image



357 Cropped Image










Flipped image

24







Cropped Image


25

359 Sharpening
















Sharpened Image

26







Gaussian Noise

Median Filtering

Contrast Image

27











0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000


0 50 100 150 200 250

Speckle Noise

Median Filtering

28



performed


threshold of 2










29

CHAPTER 4













PSNR = 10 log10

1

0

21

0

1

0

1

0

2

)()(

255

m

i

n

j

m

i

n

j

jiIjiI (41)


intensities



MSE = 1


Nminus1

0

Mminus1

0

(42)

30



NCC = --------------------------------------------------(43)





AD = -----------------------------------------------------(44)



recovered image




(46)



(47)

31


different quality

(48)

where



32




















33




Image


Barbara 4572 00174 -01054 00076 00064 233 09248 08257

Boat

4491 00209 -00898 00001 00054 230 08161 09811

Butterfly 4584 00163 -01079 00002 00061 207 09702 07058

Charlie

Chaplin

4780 00107 -04982 00001 00117 246 09432 08709

Lena 4724 00122 -03137 00009 00081 218 09595 08570

34







1 Shearing 09043 00057





6 Image flip 09069 00044







image

Inference




attacks

35

CHAPTER 5


51 CONCLUSION















52 FUTURE WORK





36

REFERENCES




























37




















373-386





52



38









Engineering

9















Advantages



10














Advantages




11












Advantages



chaos technique

12




No6





















Advantages



13



Technology



















applications

Advantages



image decryption

14

CHAPTER 3











SECRET DATA

COVER IMAGE

CHAOTIC

ENCRYPTION

ENCRY 2D LOGISTIC

ENCRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

CHAOTIC

DECRYPTION

ENCRY

SECRET DATA


DECRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

15



























and decryption

16

321 Key Generation









d= e-1

mod ( (p-1) (q-1))




322 Encryption




Cequiv me

(mod n)

323 Decryption


M equiv cd

(mod n)

17

Algorithm

























18


119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2









Algorithm






shuffling is done)

34 DNA Sequence









19





35 Attacks












1)Shearing

2) Image Scaling

3) Image Rotating


5) Image blurred

6) Image flip


8) Image sharpening


10) Image Contrast


20

351 Shearing



For example

Scale factor 09

Theta10


352 Image Scaling






For example

Scale Factor07

Theta30

scaling Image

21


353 Rotation






For Example

Theta180






Rotated image

22

colors

For Example



355 Blur Image





Fig 36 Blur Image

Color reduced image

50 100 150 200 250 300 350 400 450 500

50

100

150

200

250

300

350

400

450

500

Blurred image

23

356 Flipped Image



357 Cropped Image










Flipped image

24







Cropped Image


25

359 Sharpening
















Sharpened Image

26







Gaussian Noise

Median Filtering

Contrast Image

27











0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000


0 50 100 150 200 250

Speckle Noise

Median Filtering

28



performed


threshold of 2










29

CHAPTER 4













PSNR = 10 log10

1

0

21

0

1

0

1

0

2

)()(

255

m

i

n

j

m

i

n

j

jiIjiI (41)


intensities



MSE = 1


Nminus1

0

Mminus1

0

(42)

30



NCC = --------------------------------------------------(43)





AD = -----------------------------------------------------(44)



recovered image




(46)



(47)

31


different quality

(48)

where



32




















33




Image


Barbara 4572 00174 -01054 00076 00064 233 09248 08257

Boat

4491 00209 -00898 00001 00054 230 08161 09811

Butterfly 4584 00163 -01079 00002 00061 207 09702 07058

Charlie

Chaplin

4780 00107 -04982 00001 00117 246 09432 08709

Lena 4724 00122 -03137 00009 00081 218 09595 08570

34







1 Shearing 09043 00057





6 Image flip 09069 00044







image

Inference




attacks

35

CHAPTER 5


51 CONCLUSION















52 FUTURE WORK





36

REFERENCES




























37




















373-386





52



38









Engineering

10














Advantages




11












Advantages



chaos technique

12




No6





















Advantages



13



Technology



















applications

Advantages



image decryption

14

CHAPTER 3











SECRET DATA

COVER IMAGE

CHAOTIC

ENCRYPTION

ENCRY 2D LOGISTIC

ENCRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

CHAOTIC

DECRYPTION

ENCRY

SECRET DATA


DECRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

15



























and decryption

16

321 Key Generation









d= e-1

mod ( (p-1) (q-1))




322 Encryption




Cequiv me

(mod n)

323 Decryption


M equiv cd

(mod n)

17

Algorithm

























18


119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2









Algorithm






shuffling is done)

34 DNA Sequence









19





35 Attacks












1)Shearing

2) Image Scaling

3) Image Rotating


5) Image blurred

6) Image flip


8) Image sharpening


10) Image Contrast


20

351 Shearing



For example

Scale factor 09

Theta10


352 Image Scaling






For example

Scale Factor07

Theta30

scaling Image

21


353 Rotation






For Example

Theta180






Rotated image

22

colors

For Example



355 Blur Image





Fig 36 Blur Image

Color reduced image

50 100 150 200 250 300 350 400 450 500

50

100

150

200

250

300

350

400

450

500

Blurred image

23

356 Flipped Image



357 Cropped Image










Flipped image

24







Cropped Image


25

359 Sharpening
















Sharpened Image

26







Gaussian Noise

Median Filtering

Contrast Image

27











0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000


0 50 100 150 200 250

Speckle Noise

Median Filtering

28



performed


threshold of 2










29

CHAPTER 4













PSNR = 10 log10

1

0

21

0

1

0

1

0

2

)()(

255

m

i

n

j

m

i

n

j

jiIjiI (41)


intensities



MSE = 1


Nminus1

0

Mminus1

0

(42)

30



NCC = --------------------------------------------------(43)





AD = -----------------------------------------------------(44)



recovered image




(46)



(47)

31


different quality

(48)

where



32




















33




Image


Barbara 4572 00174 -01054 00076 00064 233 09248 08257

Boat

4491 00209 -00898 00001 00054 230 08161 09811

Butterfly 4584 00163 -01079 00002 00061 207 09702 07058

Charlie

Chaplin

4780 00107 -04982 00001 00117 246 09432 08709

Lena 4724 00122 -03137 00009 00081 218 09595 08570

34







1 Shearing 09043 00057





6 Image flip 09069 00044







image

Inference




attacks

35

CHAPTER 5


51 CONCLUSION















52 FUTURE WORK





36

REFERENCES




























37




















373-386





52



38









Engineering

11












Advantages



chaos technique

12




No6





















Advantages



13



Technology



















applications

Advantages



image decryption

14

CHAPTER 3











SECRET DATA

COVER IMAGE

CHAOTIC

ENCRYPTION

ENCRY 2D LOGISTIC

ENCRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

CHAOTIC

DECRYPTION

ENCRY

SECRET DATA


DECRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

15



























and decryption

16

321 Key Generation









d= e-1

mod ( (p-1) (q-1))




322 Encryption




Cequiv me

(mod n)

323 Decryption


M equiv cd

(mod n)

17

Algorithm

























18


119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2









Algorithm






shuffling is done)

34 DNA Sequence









19





35 Attacks












1)Shearing

2) Image Scaling

3) Image Rotating


5) Image blurred

6) Image flip


8) Image sharpening


10) Image Contrast


20

351 Shearing



For example

Scale factor 09

Theta10


352 Image Scaling






For example

Scale Factor07

Theta30

scaling Image

21


353 Rotation






For Example

Theta180






Rotated image

22

colors

For Example



355 Blur Image





Fig 36 Blur Image

Color reduced image

50 100 150 200 250 300 350 400 450 500

50

100

150

200

250

300

350

400

450

500

Blurred image

23

356 Flipped Image



357 Cropped Image










Flipped image

24







Cropped Image


25

359 Sharpening
















Sharpened Image

26







Gaussian Noise

Median Filtering

Contrast Image

27











0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000


0 50 100 150 200 250

Speckle Noise

Median Filtering

28



performed


threshold of 2










29

CHAPTER 4













PSNR = 10 log10

1

0

21

0

1

0

1

0

2

)()(

255

m

i

n

j

m

i

n

j

jiIjiI (41)


intensities



MSE = 1


Nminus1

0

Mminus1

0

(42)

30



NCC = --------------------------------------------------(43)





AD = -----------------------------------------------------(44)



recovered image




(46)



(47)

31


different quality

(48)

where



32




















33




Image


Barbara 4572 00174 -01054 00076 00064 233 09248 08257

Boat

4491 00209 -00898 00001 00054 230 08161 09811

Butterfly 4584 00163 -01079 00002 00061 207 09702 07058

Charlie

Chaplin

4780 00107 -04982 00001 00117 246 09432 08709

Lena 4724 00122 -03137 00009 00081 218 09595 08570

34







1 Shearing 09043 00057





6 Image flip 09069 00044







image

Inference




attacks

35

CHAPTER 5


51 CONCLUSION















52 FUTURE WORK





36

REFERENCES




























37




















373-386





52



38









Engineering

12




No6





















Advantages



13



Technology



















applications

Advantages



image decryption

14

CHAPTER 3











SECRET DATA

COVER IMAGE

CHAOTIC

ENCRYPTION

ENCRY 2D LOGISTIC

ENCRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

CHAOTIC

DECRYPTION

ENCRY

SECRET DATA


DECRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

15



























and decryption

16

321 Key Generation









d= e-1

mod ( (p-1) (q-1))




322 Encryption




Cequiv me

(mod n)

323 Decryption


M equiv cd

(mod n)

17

Algorithm

























18


119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2









Algorithm






shuffling is done)

34 DNA Sequence









19





35 Attacks












1)Shearing

2) Image Scaling

3) Image Rotating


5) Image blurred

6) Image flip


8) Image sharpening


10) Image Contrast


20

351 Shearing



For example

Scale factor 09

Theta10


352 Image Scaling






For example

Scale Factor07

Theta30

scaling Image

21


353 Rotation






For Example

Theta180






Rotated image

22

colors

For Example



355 Blur Image





Fig 36 Blur Image

Color reduced image

50 100 150 200 250 300 350 400 450 500

50

100

150

200

250

300

350

400

450

500

Blurred image

23

356 Flipped Image



357 Cropped Image










Flipped image

24







Cropped Image


25

359 Sharpening
















Sharpened Image

26







Gaussian Noise

Median Filtering

Contrast Image

27











0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000


0 50 100 150 200 250

Speckle Noise

Median Filtering

28



performed


threshold of 2










29

CHAPTER 4













PSNR = 10 log10

1

0

21

0

1

0

1

0

2

)()(

255

m

i

n

j

m

i

n

j

jiIjiI (41)


intensities



MSE = 1


Nminus1

0

Mminus1

0

(42)

30



NCC = --------------------------------------------------(43)





AD = -----------------------------------------------------(44)



recovered image




(46)



(47)

31


different quality

(48)

where



32




















33




Image


Barbara 4572 00174 -01054 00076 00064 233 09248 08257

Boat

4491 00209 -00898 00001 00054 230 08161 09811

Butterfly 4584 00163 -01079 00002 00061 207 09702 07058

Charlie

Chaplin

4780 00107 -04982 00001 00117 246 09432 08709

Lena 4724 00122 -03137 00009 00081 218 09595 08570

34







1 Shearing 09043 00057





6 Image flip 09069 00044







image

Inference




attacks

35

CHAPTER 5


51 CONCLUSION















52 FUTURE WORK





36

REFERENCES




























37




















373-386





52



38









Engineering

13



Technology



















applications

Advantages



image decryption

14

CHAPTER 3











SECRET DATA

COVER IMAGE

CHAOTIC

ENCRYPTION

ENCRY 2D LOGISTIC

ENCRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

CHAOTIC

DECRYPTION

ENCRY

SECRET DATA


DECRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

15



























and decryption

16

321 Key Generation









d= e-1

mod ( (p-1) (q-1))




322 Encryption




Cequiv me

(mod n)

323 Decryption


M equiv cd

(mod n)

17

Algorithm

























18


119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2









Algorithm






shuffling is done)

34 DNA Sequence









19





35 Attacks












1)Shearing

2) Image Scaling

3) Image Rotating


5) Image blurred

6) Image flip


8) Image sharpening


10) Image Contrast


20

351 Shearing



For example

Scale factor 09

Theta10


352 Image Scaling






For example

Scale Factor07

Theta30

scaling Image

21


353 Rotation






For Example

Theta180






Rotated image

22

colors

For Example



355 Blur Image





Fig 36 Blur Image

Color reduced image

50 100 150 200 250 300 350 400 450 500

50

100

150

200

250

300

350

400

450

500

Blurred image

23

356 Flipped Image



357 Cropped Image










Flipped image

24







Cropped Image


25

359 Sharpening
















Sharpened Image

26







Gaussian Noise

Median Filtering

Contrast Image

27











0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000


0 50 100 150 200 250

Speckle Noise

Median Filtering

28



performed


threshold of 2










29

CHAPTER 4













PSNR = 10 log10

1

0

21

0

1

0

1

0

2

)()(

255

m

i

n

j

m

i

n

j

jiIjiI (41)


intensities



MSE = 1


Nminus1

0

Mminus1

0

(42)

30



NCC = --------------------------------------------------(43)





AD = -----------------------------------------------------(44)



recovered image




(46)



(47)

31


different quality

(48)

where



32




















33




Image


Barbara 4572 00174 -01054 00076 00064 233 09248 08257

Boat

4491 00209 -00898 00001 00054 230 08161 09811

Butterfly 4584 00163 -01079 00002 00061 207 09702 07058

Charlie

Chaplin

4780 00107 -04982 00001 00117 246 09432 08709

Lena 4724 00122 -03137 00009 00081 218 09595 08570

34







1 Shearing 09043 00057





6 Image flip 09069 00044







image

Inference




attacks

35

CHAPTER 5


51 CONCLUSION















52 FUTURE WORK





36

REFERENCES




























37




















373-386





52



38









Engineering

14

CHAPTER 3











SECRET DATA

COVER IMAGE

CHAOTIC

ENCRYPTION

ENCRY 2D LOGISTIC

ENCRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

CHAOTIC

DECRYPTION

ENCRY

SECRET DATA


DECRYPTION

EMBEDDED

IMAGE

KEY (K1)

DNA

SEQUENCE

15



























and decryption

16

321 Key Generation









d= e-1

mod ( (p-1) (q-1))




322 Encryption




Cequiv me

(mod n)

323 Decryption


M equiv cd

(mod n)

17

Algorithm

























18


119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2









Algorithm






shuffling is done)

34 DNA Sequence









19





35 Attacks












1)Shearing

2) Image Scaling

3) Image Rotating


5) Image blurred

6) Image flip


8) Image sharpening


10) Image Contrast


20

351 Shearing



For example

Scale factor 09

Theta10


352 Image Scaling






For example

Scale Factor07

Theta30

scaling Image

21


353 Rotation






For Example

Theta180






Rotated image

22

colors

For Example



355 Blur Image





Fig 36 Blur Image

Color reduced image

50 100 150 200 250 300 350 400 450 500

50

100

150

200

250

300

350

400

450

500

Blurred image

23

356 Flipped Image



357 Cropped Image










Flipped image

24







Cropped Image


25

359 Sharpening
















Sharpened Image

26







Gaussian Noise

Median Filtering

Contrast Image

27











0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000


0 50 100 150 200 250

Speckle Noise

Median Filtering

28



performed


threshold of 2










29

CHAPTER 4













PSNR = 10 log10

1

0

21

0

1

0

1

0

2

)()(

255

m

i

n

j

m

i

n

j

jiIjiI (41)


intensities



MSE = 1


Nminus1

0

Mminus1

0

(42)

30



NCC = --------------------------------------------------(43)





AD = -----------------------------------------------------(44)



recovered image




(46)



(47)

31


different quality

(48)

where



32




















33




Image


Barbara 4572 00174 -01054 00076 00064 233 09248 08257

Boat

4491 00209 -00898 00001 00054 230 08161 09811

Butterfly 4584 00163 -01079 00002 00061 207 09702 07058

Charlie

Chaplin

4780 00107 -04982 00001 00117 246 09432 08709

Lena 4724 00122 -03137 00009 00081 218 09595 08570

34







1 Shearing 09043 00057





6 Image flip 09069 00044







image

Inference




attacks

35

CHAPTER 5


51 CONCLUSION















52 FUTURE WORK





36

REFERENCES




























37




















373-386





52



38









Engineering

15



























and decryption

16

321 Key Generation









d= e-1

mod ( (p-1) (q-1))




322 Encryption




Cequiv me

(mod n)

323 Decryption


M equiv cd

(mod n)

17

Algorithm

























18


119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2









Algorithm






shuffling is done)

34 DNA Sequence









19





35 Attacks












1)Shearing

2) Image Scaling

3) Image Rotating


5) Image blurred

6) Image flip


8) Image sharpening


10) Image Contrast


20

351 Shearing



For example

Scale factor 09

Theta10


352 Image Scaling






For example

Scale Factor07

Theta30

scaling Image

21


353 Rotation






For Example

Theta180






Rotated image

22

colors

For Example



355 Blur Image





Fig 36 Blur Image

Color reduced image

50 100 150 200 250 300 350 400 450 500

50

100

150

200

250

300

350

400

450

500

Blurred image

23

356 Flipped Image



357 Cropped Image










Flipped image

24







Cropped Image


25

359 Sharpening
















Sharpened Image

26







Gaussian Noise

Median Filtering

Contrast Image

27











0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000


0 50 100 150 200 250

Speckle Noise

Median Filtering

28



performed


threshold of 2










29

CHAPTER 4













PSNR = 10 log10

1

0

21

0

1

0

1

0

2

)()(

255

m

i

n

j

m

i

n

j

jiIjiI (41)


intensities



MSE = 1


Nminus1

0

Mminus1

0

(42)

30



NCC = --------------------------------------------------(43)





AD = -----------------------------------------------------(44)



recovered image




(46)



(47)

31


different quality

(48)

where



32




















33




Image


Barbara 4572 00174 -01054 00076 00064 233 09248 08257

Boat

4491 00209 -00898 00001 00054 230 08161 09811

Butterfly 4584 00163 -01079 00002 00061 207 09702 07058

Charlie

Chaplin

4780 00107 -04982 00001 00117 246 09432 08709

Lena 4724 00122 -03137 00009 00081 218 09595 08570

34







1 Shearing 09043 00057





6 Image flip 09069 00044







image

Inference




attacks

35

CHAPTER 5


51 CONCLUSION















52 FUTURE WORK





36

REFERENCES




























37




















373-386





52



38









Engineering

16

321 Key Generation









d= e-1

mod ( (p-1) (q-1))




322 Encryption




Cequiv me

(mod n)

323 Decryption


M equiv cd

(mod n)

17

Algorithm

























18


119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2









Algorithm






shuffling is done)

34 DNA Sequence









19





35 Attacks












1)Shearing

2) Image Scaling

3) Image Rotating


5) Image blurred

6) Image flip


8) Image sharpening


10) Image Contrast


20

351 Shearing



For example

Scale factor 09

Theta10


352 Image Scaling






For example

Scale Factor07

Theta30

scaling Image

21


353 Rotation






For Example

Theta180






Rotated image

22

colors

For Example



355 Blur Image





Fig 36 Blur Image

Color reduced image

50 100 150 200 250 300 350 400 450 500

50

100

150

200

250

300

350

400

450

500

Blurred image

23

356 Flipped Image



357 Cropped Image










Flipped image

24







Cropped Image


25

359 Sharpening
















Sharpened Image

26







Gaussian Noise

Median Filtering

Contrast Image

27











0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000


0 50 100 150 200 250

Speckle Noise

Median Filtering

28



performed


threshold of 2










29

CHAPTER 4













PSNR = 10 log10

1

0

21

0

1

0

1

0

2

)()(

255

m

i

n

j

m

i

n

j

jiIjiI (41)


intensities



MSE = 1


Nminus1

0

Mminus1

0

(42)

30



NCC = --------------------------------------------------(43)





AD = -----------------------------------------------------(44)



recovered image




(46)



(47)

31


different quality

(48)

where



32




















33




Image


Barbara 4572 00174 -01054 00076 00064 233 09248 08257

Boat

4491 00209 -00898 00001 00054 230 08161 09811

Butterfly 4584 00163 -01079 00002 00061 207 09702 07058

Charlie

Chaplin

4780 00107 -04982 00001 00117 246 09432 08709

Lena 4724 00122 -03137 00009 00081 218 09595 08570

34







1 Shearing 09043 00057





6 Image flip 09069 00044







image

Inference




attacks

35

CHAPTER 5


51 CONCLUSION















52 FUTURE WORK





36

REFERENCES




























37




















373-386





52



38









Engineering

17

Algorithm

























18


119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2









Algorithm






shuffling is done)

34 DNA Sequence









19





35 Attacks












1)Shearing

2) Image Scaling

3) Image Rotating


5) Image blurred

6) Image flip


8) Image sharpening


10) Image Contrast


20

351 Shearing



For example

Scale factor 09

Theta10


352 Image Scaling






For example

Scale Factor07

Theta30

scaling Image

21


353 Rotation






For Example

Theta180






Rotated image

22

colors

For Example



355 Blur Image





Fig 36 Blur Image

Color reduced image

50 100 150 200 250 300 350 400 450 500

50

100

150

200

250

300

350

400

450

500

Blurred image

23

356 Flipped Image



357 Cropped Image










Flipped image

24







Cropped Image


25

359 Sharpening
















Sharpened Image

26







Gaussian Noise

Median Filtering

Contrast Image

27











0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000


0 50 100 150 200 250

Speckle Noise

Median Filtering

28



performed


threshold of 2










29

CHAPTER 4













PSNR = 10 log10

1

0

21

0

1

0

1

0

2

)()(

255

m

i

n

j

m

i

n

j

jiIjiI (41)


intensities



MSE = 1


Nminus1

0

Mminus1

0

(42)

30



NCC = --------------------------------------------------(43)





AD = -----------------------------------------------------(44)



recovered image




(46)



(47)

31


different quality

(48)

where



32




















33




Image


Barbara 4572 00174 -01054 00076 00064 233 09248 08257

Boat

4491 00209 -00898 00001 00054 230 08161 09811

Butterfly 4584 00163 -01079 00002 00061 207 09702 07058

Charlie

Chaplin

4780 00107 -04982 00001 00117 246 09432 08709

Lena 4724 00122 -03137 00009 00081 218 09595 08570

34







1 Shearing 09043 00057





6 Image flip 09069 00044







image

Inference




attacks

35

CHAPTER 5


51 CONCLUSION















52 FUTURE WORK





36

REFERENCES




























37




















373-386





52



38









Engineering

18


119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2









Algorithm






shuffling is done)

34 DNA Sequence









19





35 Attacks












1)Shearing

2) Image Scaling

3) Image Rotating


5) Image blurred

6) Image flip


8) Image sharpening


10) Image Contrast


20

351 Shearing



For example

Scale factor 09

Theta10


352 Image Scaling






For example

Scale Factor07

Theta30

scaling Image

21


353 Rotation






For Example

Theta180






Rotated image

22

colors

For Example



355 Blur Image





Fig 36 Blur Image

Color reduced image

50 100 150 200 250 300 350 400 450 500

50

100

150

200

250

300

350

400

450

500

Blurred image

23

356 Flipped Image



357 Cropped Image










Flipped image

24







Cropped Image


25

359 Sharpening
















Sharpened Image

26







Gaussian Noise

Median Filtering

Contrast Image

27











0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000


0 50 100 150 200 250

Speckle Noise

Median Filtering

28



performed


threshold of 2










29

CHAPTER 4













PSNR = 10 log10

1

0

21

0

1

0

1

0

2

)()(

255

m

i

n

j

m

i

n

j

jiIjiI (41)


intensities



MSE = 1


Nminus1

0

Mminus1

0

(42)

30



NCC = --------------------------------------------------(43)





AD = -----------------------------------------------------(44)



recovered image




(46)



(47)

31


different quality

(48)

where



32




















33




Image


Barbara 4572 00174 -01054 00076 00064 233 09248 08257

Boat

4491 00209 -00898 00001 00054 230 08161 09811

Butterfly 4584 00163 -01079 00002 00061 207 09702 07058

Charlie

Chaplin

4780 00107 -04982 00001 00117 246 09432 08709

Lena 4724 00122 -03137 00009 00081 218 09595 08570

34







1 Shearing 09043 00057





6 Image flip 09069 00044







image

Inference




attacks

35

CHAPTER 5


51 CONCLUSION















52 FUTURE WORK





36

REFERENCES




























37




















373-386





52



38









Engineering

19





35 Attacks












1)Shearing

2) Image Scaling

3) Image Rotating


5) Image blurred

6) Image flip


8) Image sharpening


10) Image Contrast


20

351 Shearing



For example

Scale factor 09

Theta10


352 Image Scaling






For example

Scale Factor07

Theta30

scaling Image

21


353 Rotation






For Example

Theta180






Rotated image

22

colors

For Example



355 Blur Image





Fig 36 Blur Image

Color reduced image

50 100 150 200 250 300 350 400 450 500

50

100

150

200

250

300

350

400

450

500

Blurred image

23

356 Flipped Image



357 Cropped Image










Flipped image

24







Cropped Image


25

359 Sharpening
















Sharpened Image

26







Gaussian Noise

Median Filtering

Contrast Image

27











0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000


0 50 100 150 200 250

Speckle Noise

Median Filtering

28



performed


threshold of 2










29

CHAPTER 4













PSNR = 10 log10

1

0

21

0

1

0

1

0

2

)()(

255

m

i

n

j

m

i

n

j

jiIjiI (41)


intensities



MSE = 1


Nminus1

0

Mminus1

0

(42)

30



NCC = --------------------------------------------------(43)





AD = -----------------------------------------------------(44)



recovered image




(46)



(47)

31


different quality

(48)

where



32




















33




Image


Barbara 4572 00174 -01054 00076 00064 233 09248 08257

Boat

4491 00209 -00898 00001 00054 230 08161 09811

Butterfly 4584 00163 -01079 00002 00061 207 09702 07058

Charlie

Chaplin

4780 00107 -04982 00001 00117 246 09432 08709

Lena 4724 00122 -03137 00009 00081 218 09595 08570

34







1 Shearing 09043 00057





6 Image flip 09069 00044







image

Inference




attacks

35

CHAPTER 5


51 CONCLUSION















52 FUTURE WORK





36

REFERENCES




























37




















373-386





52



38









Engineering

20

351 Shearing



For example

Scale factor 09

Theta10


352 Image Scaling






For example

Scale Factor07

Theta30

scaling Image

21


353 Rotation






For Example

Theta180






Rotated image

22

colors

For Example



355 Blur Image





Fig 36 Blur Image

Color reduced image

50 100 150 200 250 300 350 400 450 500

50

100

150

200

250

300

350

400

450

500

Blurred image

23

356 Flipped Image



357 Cropped Image










Flipped image

24







Cropped Image


25

359 Sharpening
















Sharpened Image

26







Gaussian Noise

Median Filtering

Contrast Image

27











0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000


0 50 100 150 200 250

Speckle Noise

Median Filtering

28



performed


threshold of 2










29

CHAPTER 4













PSNR = 10 log10

1

0

21

0

1

0

1

0

2

)()(

255

m

i

n

j

m

i

n

j

jiIjiI (41)


intensities



MSE = 1


Nminus1

0

Mminus1

0

(42)

30



NCC = --------------------------------------------------(43)





AD = -----------------------------------------------------(44)



recovered image




(46)



(47)

31


different quality

(48)

where



32




















33




Image


Barbara 4572 00174 -01054 00076 00064 233 09248 08257

Boat

4491 00209 -00898 00001 00054 230 08161 09811

Butterfly 4584 00163 -01079 00002 00061 207 09702 07058

Charlie

Chaplin

4780 00107 -04982 00001 00117 246 09432 08709

Lena 4724 00122 -03137 00009 00081 218 09595 08570

34







1 Shearing 09043 00057





6 Image flip 09069 00044







image

Inference




attacks

35

CHAPTER 5


51 CONCLUSION















52 FUTURE WORK





36

REFERENCES




























37




















373-386





52



38









Engineering

21


353 Rotation






For Example

Theta180






Rotated image

22

colors

For Example



355 Blur Image





Fig 36 Blur Image

Color reduced image

50 100 150 200 250 300 350 400 450 500

50

100

150

200

250

300

350

400

450

500

Blurred image

23

356 Flipped Image



357 Cropped Image










Flipped image

24







Cropped Image


25

359 Sharpening
















Sharpened Image

26







Gaussian Noise

Median Filtering

Contrast Image

27











0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000


0 50 100 150 200 250

Speckle Noise

Median Filtering

28



performed


threshold of 2










29

CHAPTER 4













PSNR = 10 log10

1

0

21

0

1

0

1

0

2

)()(

255

m

i

n

j

m

i

n

j

jiIjiI (41)


intensities



MSE = 1


Nminus1

0

Mminus1

0

(42)

30



NCC = --------------------------------------------------(43)





AD = -----------------------------------------------------(44)



recovered image




(46)



(47)

31


different quality

(48)

where



32




















33




Image


Barbara 4572 00174 -01054 00076 00064 233 09248 08257

Boat

4491 00209 -00898 00001 00054 230 08161 09811

Butterfly 4584 00163 -01079 00002 00061 207 09702 07058

Charlie

Chaplin

4780 00107 -04982 00001 00117 246 09432 08709

Lena 4724 00122 -03137 00009 00081 218 09595 08570

34







1 Shearing 09043 00057





6 Image flip 09069 00044







image

Inference




attacks

35

CHAPTER 5


51 CONCLUSION















52 FUTURE WORK





36

REFERENCES




























37




















373-386





52



38









Engineering

22

colors

For Example



355 Blur Image





Fig 36 Blur Image

Color reduced image

50 100 150 200 250 300 350 400 450 500

50

100

150

200

250

300

350

400

450

500

Blurred image

23

356 Flipped Image



357 Cropped Image










Flipped image

24







Cropped Image


25

359 Sharpening
















Sharpened Image

26







Gaussian Noise

Median Filtering

Contrast Image

27











0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000


0 50 100 150 200 250

Speckle Noise

Median Filtering

28



performed


threshold of 2










29

CHAPTER 4













PSNR = 10 log10

1

0

21

0

1

0

1

0

2

)()(

255

m

i

n

j

m

i

n

j

jiIjiI (41)


intensities



MSE = 1


Nminus1

0

Mminus1

0

(42)

30



NCC = --------------------------------------------------(43)





AD = -----------------------------------------------------(44)



recovered image




(46)



(47)

31


different quality

(48)

where



32




















33




Image


Barbara 4572 00174 -01054 00076 00064 233 09248 08257

Boat

4491 00209 -00898 00001 00054 230 08161 09811

Butterfly 4584 00163 -01079 00002 00061 207 09702 07058

Charlie

Chaplin

4780 00107 -04982 00001 00117 246 09432 08709

Lena 4724 00122 -03137 00009 00081 218 09595 08570

34







1 Shearing 09043 00057





6 Image flip 09069 00044







image

Inference




attacks

35

CHAPTER 5


51 CONCLUSION















52 FUTURE WORK





36

REFERENCES




























37




















373-386





52



38









Engineering

23

356 Flipped Image



357 Cropped Image










Flipped image

24







Cropped Image


25

359 Sharpening
















Sharpened Image

26







Gaussian Noise

Median Filtering

Contrast Image

27











0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000


0 50 100 150 200 250

Speckle Noise

Median Filtering

28



performed


threshold of 2










29

CHAPTER 4













PSNR = 10 log10

1

0

21

0

1

0

1

0

2

)()(

255

m

i

n

j

m

i

n

j

jiIjiI (41)


intensities



MSE = 1


Nminus1

0

Mminus1

0

(42)

30



NCC = --------------------------------------------------(43)





AD = -----------------------------------------------------(44)



recovered image




(46)



(47)

31


different quality

(48)

where



32




















33




Image


Barbara 4572 00174 -01054 00076 00064 233 09248 08257

Boat

4491 00209 -00898 00001 00054 230 08161 09811

Butterfly 4584 00163 -01079 00002 00061 207 09702 07058

Charlie

Chaplin

4780 00107 -04982 00001 00117 246 09432 08709

Lena 4724 00122 -03137 00009 00081 218 09595 08570

34







1 Shearing 09043 00057





6 Image flip 09069 00044







image

Inference




attacks

35

CHAPTER 5


51 CONCLUSION















52 FUTURE WORK





36

REFERENCES




























37




















373-386





52



38









Engineering

24







Cropped Image


25

359 Sharpening
















Sharpened Image

26







Gaussian Noise

Median Filtering

Contrast Image

27











0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000


0 50 100 150 200 250

Speckle Noise

Median Filtering

28



performed


threshold of 2










29

CHAPTER 4













PSNR = 10 log10

1

0

21

0

1

0

1

0

2

)()(

255

m

i

n

j

m

i

n

j

jiIjiI (41)


intensities



MSE = 1


Nminus1

0

Mminus1

0

(42)

30



NCC = --------------------------------------------------(43)





AD = -----------------------------------------------------(44)



recovered image




(46)



(47)

31


different quality

(48)

where



32




















33




Image


Barbara 4572 00174 -01054 00076 00064 233 09248 08257

Boat

4491 00209 -00898 00001 00054 230 08161 09811

Butterfly 4584 00163 -01079 00002 00061 207 09702 07058

Charlie

Chaplin

4780 00107 -04982 00001 00117 246 09432 08709

Lena 4724 00122 -03137 00009 00081 218 09595 08570

34







1 Shearing 09043 00057





6 Image flip 09069 00044







image

Inference




attacks

35

CHAPTER 5


51 CONCLUSION















52 FUTURE WORK





36

REFERENCES




























37




















373-386





52



38









Engineering

25

359 Sharpening
















Sharpened Image

26







Gaussian Noise

Median Filtering

Contrast Image

27











0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000


0 50 100 150 200 250

Speckle Noise

Median Filtering

28



performed


threshold of 2










29

CHAPTER 4













PSNR = 10 log10

1

0

21

0

1

0

1

0

2

)()(

255

m

i

n

j

m

i

n

j

jiIjiI (41)


intensities



MSE = 1


Nminus1

0

Mminus1

0

(42)

30



NCC = --------------------------------------------------(43)





AD = -----------------------------------------------------(44)



recovered image




(46)



(47)

31


different quality

(48)

where



32




















33




Image


Barbara 4572 00174 -01054 00076 00064 233 09248 08257

Boat

4491 00209 -00898 00001 00054 230 08161 09811

Butterfly 4584 00163 -01079 00002 00061 207 09702 07058

Charlie

Chaplin

4780 00107 -04982 00001 00117 246 09432 08709

Lena 4724 00122 -03137 00009 00081 218 09595 08570

34







1 Shearing 09043 00057





6 Image flip 09069 00044







image

Inference




attacks

35

CHAPTER 5


51 CONCLUSION















52 FUTURE WORK





36

REFERENCES




























37




















373-386





52



38









Engineering

26







Gaussian Noise

Median Filtering

Contrast Image

27











0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000


0 50 100 150 200 250

Speckle Noise

Median Filtering

28



performed


threshold of 2










29

CHAPTER 4













PSNR = 10 log10

1

0

21

0

1

0

1

0

2

)()(

255

m

i

n

j

m

i

n

j

jiIjiI (41)


intensities



MSE = 1


Nminus1

0

Mminus1

0

(42)

30



NCC = --------------------------------------------------(43)





AD = -----------------------------------------------------(44)



recovered image




(46)



(47)

31


different quality

(48)

where



32




















33




Image


Barbara 4572 00174 -01054 00076 00064 233 09248 08257

Boat

4491 00209 -00898 00001 00054 230 08161 09811

Butterfly 4584 00163 -01079 00002 00061 207 09702 07058

Charlie

Chaplin

4780 00107 -04982 00001 00117 246 09432 08709

Lena 4724 00122 -03137 00009 00081 218 09595 08570

34







1 Shearing 09043 00057





6 Image flip 09069 00044







image

Inference




attacks

35

CHAPTER 5


51 CONCLUSION















52 FUTURE WORK





36

REFERENCES




























37




















373-386





52



38









Engineering

27











0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000


0 50 100 150 200 250

Speckle Noise

Median Filtering

28



performed


threshold of 2










29

CHAPTER 4













PSNR = 10 log10

1

0

21

0

1

0

1

0

2

)()(

255

m

i

n

j

m

i

n

j

jiIjiI (41)


intensities



MSE = 1


Nminus1

0

Mminus1

0

(42)

30



NCC = --------------------------------------------------(43)





AD = -----------------------------------------------------(44)



recovered image




(46)



(47)

31


different quality

(48)

where



32




















33




Image


Barbara 4572 00174 -01054 00076 00064 233 09248 08257

Boat

4491 00209 -00898 00001 00054 230 08161 09811

Butterfly 4584 00163 -01079 00002 00061 207 09702 07058

Charlie

Chaplin

4780 00107 -04982 00001 00117 246 09432 08709

Lena 4724 00122 -03137 00009 00081 218 09595 08570

34







1 Shearing 09043 00057





6 Image flip 09069 00044







image

Inference




attacks

35

CHAPTER 5


51 CONCLUSION















52 FUTURE WORK





36

REFERENCES




























37




















373-386





52



38









Engineering

28



performed


threshold of 2










29

CHAPTER 4













PSNR = 10 log10

1

0

21

0

1

0

1

0

2

)()(

255

m

i

n

j

m

i

n

j

jiIjiI (41)


intensities



MSE = 1


Nminus1

0

Mminus1

0

(42)

30



NCC = --------------------------------------------------(43)





AD = -----------------------------------------------------(44)



recovered image




(46)



(47)

31


different quality

(48)

where



32




















33




Image


Barbara 4572 00174 -01054 00076 00064 233 09248 08257

Boat

4491 00209 -00898 00001 00054 230 08161 09811

Butterfly 4584 00163 -01079 00002 00061 207 09702 07058

Charlie

Chaplin

4780 00107 -04982 00001 00117 246 09432 08709

Lena 4724 00122 -03137 00009 00081 218 09595 08570

34







1 Shearing 09043 00057





6 Image flip 09069 00044







image

Inference




attacks

35

CHAPTER 5


51 CONCLUSION















52 FUTURE WORK





36

REFERENCES




























37




















373-386





52



38









Engineering

29

CHAPTER 4













PSNR = 10 log10

1

0

21

0

1

0

1

0

2

)()(

255

m

i

n

j

m

i

n

j

jiIjiI (41)


intensities



MSE = 1


Nminus1

0

Mminus1

0

(42)

30



NCC = --------------------------------------------------(43)





AD = -----------------------------------------------------(44)



recovered image




(46)



(47)

31


different quality

(48)

where



32




















33




Image


Barbara 4572 00174 -01054 00076 00064 233 09248 08257

Boat

4491 00209 -00898 00001 00054 230 08161 09811

Butterfly 4584 00163 -01079 00002 00061 207 09702 07058

Charlie

Chaplin

4780 00107 -04982 00001 00117 246 09432 08709

Lena 4724 00122 -03137 00009 00081 218 09595 08570

34







1 Shearing 09043 00057





6 Image flip 09069 00044







image

Inference




attacks

35

CHAPTER 5


51 CONCLUSION















52 FUTURE WORK





36

REFERENCES




























37




















373-386





52



38









Engineering

30



NCC = --------------------------------------------------(43)





AD = -----------------------------------------------------(44)



recovered image




(46)



(47)

31


different quality

(48)

where



32




















33




Image


Barbara 4572 00174 -01054 00076 00064 233 09248 08257

Boat

4491 00209 -00898 00001 00054 230 08161 09811

Butterfly 4584 00163 -01079 00002 00061 207 09702 07058

Charlie

Chaplin

4780 00107 -04982 00001 00117 246 09432 08709

Lena 4724 00122 -03137 00009 00081 218 09595 08570

34







1 Shearing 09043 00057





6 Image flip 09069 00044







image

Inference




attacks

35

CHAPTER 5


51 CONCLUSION















52 FUTURE WORK





36

REFERENCES




























37




















373-386





52



38









Engineering

31


different quality

(48)

where



32




















33




Image


Barbara 4572 00174 -01054 00076 00064 233 09248 08257

Boat

4491 00209 -00898 00001 00054 230 08161 09811

Butterfly 4584 00163 -01079 00002 00061 207 09702 07058

Charlie

Chaplin

4780 00107 -04982 00001 00117 246 09432 08709

Lena 4724 00122 -03137 00009 00081 218 09595 08570

34







1 Shearing 09043 00057





6 Image flip 09069 00044







image

Inference




attacks

35

CHAPTER 5


51 CONCLUSION















52 FUTURE WORK





36

REFERENCES




























37




















373-386





52



38









Engineering

32




















33




Image


Barbara 4572 00174 -01054 00076 00064 233 09248 08257

Boat

4491 00209 -00898 00001 00054 230 08161 09811

Butterfly 4584 00163 -01079 00002 00061 207 09702 07058

Charlie

Chaplin

4780 00107 -04982 00001 00117 246 09432 08709

Lena 4724 00122 -03137 00009 00081 218 09595 08570

34







1 Shearing 09043 00057





6 Image flip 09069 00044







image

Inference




attacks

35

CHAPTER 5


51 CONCLUSION















52 FUTURE WORK





36

REFERENCES




























37




















373-386





52



38









Engineering

33




Image


Barbara 4572 00174 -01054 00076 00064 233 09248 08257

Boat

4491 00209 -00898 00001 00054 230 08161 09811

Butterfly 4584 00163 -01079 00002 00061 207 09702 07058

Charlie

Chaplin

4780 00107 -04982 00001 00117 246 09432 08709

Lena 4724 00122 -03137 00009 00081 218 09595 08570

34







1 Shearing 09043 00057





6 Image flip 09069 00044







image

Inference




attacks

35

CHAPTER 5


51 CONCLUSION















52 FUTURE WORK





36

REFERENCES




























37




















373-386





52



38









Engineering

34







1 Shearing 09043 00057





6 Image flip 09069 00044







image

Inference




attacks

35

CHAPTER 5


51 CONCLUSION















52 FUTURE WORK





36

REFERENCES




























37




















373-386





52



38









Engineering

35

CHAPTER 5


51 CONCLUSION















52 FUTURE WORK





36

REFERENCES




























37




















373-386





52



38









Engineering

36

REFERENCES




























37




















373-386





52



38









Engineering

37




















373-386





52



38









Engineering

38









Engineering

REVERSIBLE DATA HIDING USING CHAOTIC AND 2D LOGISTIC ... · BONAFIDE CERTIFICATE Certified that...

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Transcript of REVERSIBLE DATA HIDING USING CHAOTIC AND 2D LOGISTIC ... · BONAFIDE CERTIFICATE Certified that...