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Copyright Protection for Digital Images
Using Digital Watermarking
By
Muhammad Asim
MCS07133009
MS Computer Science
The University of Lahore Sargodha Campus - Pakistan
Fall
2015
ii
The University of Lahore
Copyright Protection for Digital Images Using Digital Watermarking
A Thesis Presented to
The University of Lahore, Sargodha Campus
In partial fulfillment of the requirement for the degree of
MS Computer Science
By
Muhammd Asim
MCS07133009
Fall 2015
iii
Copyright Protection for Digital Images
Using Digital Watermarking
A Post Graduate Thesis submitted to the Department of Computer Science as
partial fulfillment of the requirement for the award of Degree of M.S Computer
Science.
Name
Registration Number
Muhammad Asim
MCS07133009
Supervisor
Mr. Tahir Nawaz
Assistant Professor Department of Computer Science
The University of Lahore
Sargodha Campus.
January, 2016.
iv
Final Approval
This thesis titled
Copyright Protection for Digital Images
Using Digital Watermarking
By
Muhammad Asim
MCS07133009
Has been approved
For The University of Lahore, Sargodha Campus
External Examiner:
Supervisor:
Mr. Tahir Nawaz
Assistant Professor Department of Computer Science
HoD:
Prof. Dr .Parvez khan Rabbani
HoD Department of Computer Science
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Declaration
Muhammad Asim MCS07133009 hereby declare that I have produced the work
presented in this thesis, during the scheduled period of study. I also declare that I have
not taken any material from any source except referred to wherever due that amount of
plagiarism is within acceptable range. If a violation of HEC rules on research has
occurred in this thesis, I shall be liable to punishable action under the plagiarism rules
of the HEC.
Dated: Signature of Student
Muhammad Asim
MCS07133009
1-vi
Certificate
It is certified that Muhammad Asim MCS07133009 has carried out all the work related
to this thesis under my supervision at the Department of Computer Science, The
University of Lahore, Sargodha campus and the work fulfills the requirement for award of
MS degree.
Dated: Supervisor:
Mr. Tahir Nawaz
Assistant Professor
Head of Department:
Prof. Dr. Pervaiz khan Rabbani
HoD Department of Computer Science
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DEDICATION
I dedicate this work to my beloved parents, whose devotion for knowledge and
research has always been greatest source of inspiration behind my academic success.
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ACKNOWLEDGEMENTS
I most humbly pray to Allah Almighty for His unbounded bounties bestowed
upon me to acquire and disseminate it in the form of this thesis. Foremost, I
would like to express my sincere gratitude to my supervisor Mr.Tahir Nawaz,
who had been gracious enough to guide and supervise my research process
for MS thesis. His guidance helped me in all the time of research and writing
of this thesis. Besides my advisor, I would like to thank Prof. Dr.Pervaiz khan Rabbani ,
Professor of CS & IT department, for his guidance and
immense knowledge. I am also grateful to many people who directly and indirectly helped me
with the development and production of this thesis. Last but
not the least, I would like to thank my family: my parents, for their endless
love, prayers and their continuous support throughout my life.
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x
Copyright Protection for Digital Images
Using Digital Watermarking
Abstract
Digital watermarking technique is the hidden information for the protection of copyright.
Digital watermarking technique is a technique to allow a cover image to be embedded with a
watermark image, for the protection and security purpose for the ownership or for image
hiding. In this study, a Singular Value Decomposition (SVD) based digital watermarking
technique using interpolation method is proposed where the hidden information is added in
the singular values of digital images using interpolation method. The desire watermarked
image is treated with different aspects of distortion operations. Finally, the watermark is
extracted from digital images for each method and each extracted watermark image is then
compared with the original watermark by using normalized correlation method, peak signal to
noise ratio and accuracy rate is also measured. A better scheme is also applied based on
comparison results. Finally, the better technique is implemented in real world application for
the purpose of copyright protection for digital images, authentication, integrity etc.
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TABLE OF CONTENTS
Table of Contents Type chapter title (level 1) .................................................................................................................... 1
Type chapter title (level 2) ................................................................................................................. 2
Type chapter title (level 3) ............................................................................................................. 3
Type chapter title (level 1) .................................................................................................................... 4
Type chapter title (level 2) ................................................................................................................. 5
Type chapter title (level 3) ............................................................................................................. 6
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List of Figures
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List of Tables
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LIST OF ABBREVIATIONS
SVD Singular Value Decomposition
DIW Digital Image Watermark
NC Normalized Correlation
NCC Normalized Correlation co- ordination
CP Correct Pixel
NP No.of Pixel
AR Accuracy Rate
PSNR Peak Signal to Noise Ratio
DCT Discrete Cosine Transform
DWT Discrete Wavelet Transform
MSE Mean Squared Error
JAMA Java Matrix
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Chapter 1
1. Introduction
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The increasing to the access of digital images through internet and increasing the powerful
editing tool of digital images is an important issue nowadays for the owner’ [1]. A digital
watermark is defined as embedding the additional information into the host image for the
purpose of verification and authentication for the owner’s. Digital watermark can be visible
and invisible, a visible watermark can be typical a visible message on the image or on the
logos of the company and invisible watermark is hidden information in the image which is
for verification and authentication for the protection of copy right. Our work is on the digital
image copy right protection using Singular value decomposition method (SVD). This work
has already done but using different algorithms. Those algorithms have some issues i.e. time
complexity, difficult for embedding the watermark for developer etc. SVD is based on Linear
Algebra which is most efficient method. There are many types of watermarking, audio
watermark, text watermark, image watermark etc. In audio watermark, an additional audio
sound is added in existing audio materials [2]. Robust and transparent watermarking scheme
is also proposed for color images
1.1 Problem Statement
The new technologies enable us to process digital content with lesser time, lower complexities
and better efficiency. However, digitization also brings in disadvantages like illegal
reproduction and distribution of digital content. Nowadays, the spreading of digital
multimedia has made copyright protection a necessity. Too more issues like authentication
and information hiding have also become important issues.
To solve these kinds of authentication and copyright issues, various methods have been
developed to protect the digital content. This study will protect the copyright digital images
using digital watermarking. This technique should resist most of the modifications and
preserve the embedded information.
1.2 Objectives
Digital contents are rapidly increasing in the whole world day by day and protection is
necessary for multimedia contents. Multimedia included text, audio, video, and images should
be secure and protect from unauthorized user. A digital watermarking is used to protect the
multimedia contents. Digital watermark is embedded into the original image, text, and audio
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or in video which is almost invisible. Watermark is a minor modification in original image
which is invisible and a secret information is embedded into the content. Our Aims and
Objectives is to protect copyright for digital images for the vendor’s.
1.3 Research Questions
This thesis will answers the following questions
1. The proposed technique will facilitate the digital images?
2. The digital watermarking technique is similar to as steganography?
3. The watermark technique will implemented for real life applications?
1.4 Scope To protect the
Copy right
1.5 Methodology
This study is based upon the updated literature review that identified digital watermarking in
multimedia for the protection of copyright. Digital watermarking algorithm is based on two
mathematical techniques. First is Singular value decomposition (SVD) and interpolation
method viz. Linear algebra and cosine interpolation method.
A cover image and watermark image is decomposed into respective SVD. After this, the
singular value of watermark image is found by using interpolation method. This study will be
implemented in matlab tool.
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,
1.6 Thesis Layout
This thesis presents a detailed study of digital watermarking techniques and its literature
reviews, problem statement and its solution by the proposed watermark technique,
simulations and results and a brief comparison of results with the conventional hidden
information techniques and digital watermarking techniques.
• Chapter 1: Introduction
In this chapter, introduction to the problem associated with protection for the
digital images, research objectives and methodology are briefly described. The
research questions that need to be answered in this project are also discussed.
• Chapter 2: Literature Review
A lot of work has been done in the literature about digital watermarking
techniques. Those techniques which are found to be efficient and effective are
described in this chapter.
• Chapter 3:
In this chapter, background theory related to the project and technological review
have been presented.
• Chapter 4: Proposed Technique
In this chapter, the proposed digital watermark technique and its implementation
and model are presented.
• Chapter 5: Simulation and Results
In this chapter, the simulation of the proposed digital watermark technique has
been carried out and it has been compared with conventional hidden information
techniques.
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• Chapter 6: Conclusion and Future work
In this chapter, the key findings of the research are described briefly in the
conclusion. Since there is always a room for improvements, therefore, the
suggestions and goals for further research based on this project are also discussed
in Future Work.
• References:
At the end of the thesis, references of research materials such as research papers,
thesis and international proceedings etc. which have been helpful in completion
of this project, are provided for detailed study.
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Chapter 2
Literature Review
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In early years, encryption and control access techniques were used to protect
ownership of media. But nowadays in digital world, watermark technique is
introduced to keep the copyright of multimedia. Digital watermark is to embedding
a special hidden pattern in multimedia using some algorithms. DCT-based and
wavelet-based technique are mostly used in watermark. Watermark can be measured
using similarity based and data correlation based methods [3].
2.1 Digital Image watermarking
“Digital watermarking” is a process of hiding digital information in a carrier signal
[4] [5]. Digital image watermarking embedding some information in cover image
before it is globally posted. Embedding watermarking in cover image show in below
figure.
Figure 1: Block diagram of watermarking process
The information to be embedded in the digital image is called a digital watermark.
The signal where the watermark is to be embedded is called the host signal.
The watermark process is divided into three distinct steps which are given below.
I. Embedding
II. Attack
III. Detection/extraction
Watermark is embedded in grayscale images which are used for valuable
applications. A model HVS (Human visual system) is trained using Fuzzy-BPN
hybrid technique to produced novel image watermark scheme to embed the
grayscale images [4].
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Digital image watermarking techniques have been presented using fuzzy logic and
tabu search. In this approach image is divided into blocks and then parametric slant-
Hadamard transform is applied to each block individually. Then the watermark is
embedding in the transform domain and inverse transform is obtained. The selected
transform includes some parameters such as robustness and imperceptibility. The
robustness and imperceptibility are conflict to each other, however apply the
transform parameters to enhance the robustness by tabu search and the embedding
the watermark into image [5].
Software copy right using hiding watermarks in various data structures used by the
code B+-Trees, R-Trees, Linked lists. The main problem is that embedding of the
watermarking is should have minimum effect on the operations and performance of
data structures. There are two watermarking techniques. One is static watermarking
and second one is dynamic watermarking. In static watermarking the water is stored
in the source code, may be in data section or in the code section and the second
technique is dynamic watermarking stores the watermark in the program execution
static. Hiding watermark in data structures, which are used by the program, would
make the more robust [6].
The large datasets which are generated from very large databases are being mined
to take out secret knowledge and patterns which are useful for decision making. A
model has been developed for this special purpose, a framework model which
develop usability constraints for all types of datasets. The second one is novel
watermarking technique that facilitates for, non-commercial and strings datasets.
Watermarking scheme not only preserve the classification potential of features but
also results in approximately zero information loss. Two main phases in these
watermarking scheme one is watermark encoding and second one is watermark
decoding [8]. An audio watermarking method using ideas of wavelet-based entropy.
The method converts low-frequency coefficients of discrete wavelet transform
(DWT) into wavelet based entropy (WBE) domain. This process followed by the
calculations of mean values of each audio as well as derivation of some essential
properties of WBE, watermark can be applied on using only values of the WBE [9].
A novel blind watermark technique has been introduced based on the back
propagation and artificial neural network is proposed. The proposed watermarking
on HVS images in which take inputs with its weights and then applied a threshold
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then trained the whole model and produced the output with embedding watermark.
This method is mostly applied on image processing [7]. A novel algorithms for blind
watermarking has been introduced to protect the copy right issues and other
multimedia areas, by applying singular value decomposition (SVD) and least square
support vector machine (SQ-SVM) into watermark embedding and detection. In this,
two process are encoding and decoding, in coding process watermark is embed and
in decoding process extract the embedding watermark using support vector machine
[8]. Digital watermark has been proposed for the protection of copyright detection
between seller and buyer. A content owner (seller) inserts the unique watermark into
copy of content before it to be sold. If the buyer sells unauthorized copy of content
then it can be detect using watermark detection algorithm [9]. A joint
encryption/watermarking system with more security based on double random based
(DREP) in quaternion gyrator transform domain is addressed. In this proposed
scheme, a RGB-scale watermark image together is encoded [10]. The spread-
spectrum like discrete cosine transform domain (DCT) watermarking technique for
copyright protection for still digital images. In this technique, DCT is applied on
blocks 8*8 pixels on images as in JPEG algorithm. The watermark can encode
information to protect the illegal misuse [11]. The robustness and the
imperceptibility are the basic requirements of the digital watermarking. The main
key factor which effect the robustness and the imperceptibility is its strength.
Artificial neural network is make its strength [12]. There are two characteristics of
perfect digital watermarking. One is robustness that is geometric attacks which effect
on the image, i.e. image compression. And the other one is preserve the image
quality after embedding the watermark. The requirement robustness and the image
quality is affect to each other. An optimum solution has been found to tackle this
problem that is game theoretic approach. Game theory is formulate the dynamic non-
cooperative complete information in which one player has the complete information
of the other player but it is not necessary the actions [13] .
Fingerprint watermark technique is also used for the protection of copyright
detection. It is also known as watermarking using biometric system. In this process
embedding the fingerprint using the novel method of empirical mode decomposition
(EMD) and gene expression program (GEP) provide together. The watermarking
algorithm uses singular value decomposition (SVD) and lifting based discrete
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wavelet transform (DWT). This method is more secure, robust and imperceptible
watermarking algorithm. If the logos protection, which are protected by
watermarking biometric scheme which is more protected and secure than the other
watermarking techniques [14].
Wavelet-tree-based watermarking technique is also used for copyright protection.
Host image divided into groups so called supper trees. The watermark is embedded
by quantized supper trees. In this technique watermark is embedded in each bit
frequency domain which is minor modification in original image which is almost
invisible in original image [15].
The watermark is also used in databases to improve the database integrity. Meng-
Hsiun Tsai et al. introduced the database watermark technique for temper detection.
In this technique, there are there steps to complete this algorithms. First, MD5
algorithm is used to fetch database extraction. Second, XOR operation applied on
extraction database and digital watermarking gets a certification number. At last,
encrypt the certification number and makes public in network with database. Before
using this, user needs to use database owner’s database encrypt certification number.
In the last step, if the watermark complete at the end user then it proved the database
integrity [16].
A fragile watermark can also identify where the original image has been altered.
Another watermark is variable-water mark two dimensional algorithm (VW2D) is
also used in this approach [17].
Digital watermarking is also used for files posting on the internet. In the recent years
growing the social networking websites has highlighted the importance of protection
and security of digital products. These digital products are also called digital
contents, such as digital videos, games, computer animation, actions, service
learning, digital publishing, digital publishing, content, software, and network
services. On social networking, people share their post, photos, videos which
demands the security. After analyzed that, it was found that female respondents often
share digital contents than male respondents. So they need more protection than male
respondents. A digital technique is used to tackle this issues on the basis of some
questionnaire. Digital watermarking as a sign with the figures of copyright set to
digital watermarking to set their picture protection. Questionnaire based on personal
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information, name, age, occupation, sex. After these analysis, the result was showed
that the female and worker sharing content more than other [18].
The camera in smartphones is one of the most widely used functions nowadays.
Millions of people used camera in smartphones in whole world and take pictures,
recording audio and videos and share these digital contents to their families,
relatives, and friends through different social network. This unprecedented uses
produces huge number of multimedia contents such as audio, videos and clips
sharing on social network might create intellectual property [IP] issues. Shared
images, videos and clips on social networks without owner’s permission. To tackle
this issue a digital signature based technique is introduced in which owner’s add
their signature on digital contents. This technique is more efficient and flexible to
protect the digital contents. This watermarking system is recently introduced in
android smartphones, the software is called lollipop [19].
Mobile phone numbers are also used for the protection of digital images captured by
the mobile phones. Because mobile phone number is a unique number all over the
world. Mobile phone number is embedding in the digital image captured by the
owner’s phone Binary coded decimal (BCD) encoder. This research has been
proposed in UAE people in which 14 digit phone number is converted into 4-bit
binary number. Then the checksum of phone number is added to this 4-bit binary
number to generate checksum binary vector. This checksum can be used during the
decoding process to verify the correctness of extracted phone number [20].
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Chapter 3
Proposed Technique
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This study is based upon the updated literature review that identified digital
watermarking in multimedia for the protection of copyright. Digital watermarking
algorithm is based on two mathematical techniques. First is Singular value
decomposition (SVD) and interpolation method viz. Linear algebra and cosine
interpolation method.
A cover image and watermark image is decomposed into respective SVD. After this,
the singular value of watermark image is found by using interpolation method. After
this, the inverse SVD (Singular Value Decomposition) is performed. Then various
attacks were applied on digital watermark cover image. After this process the
watermark image was extracted from the distorted image and then compared with
the original image.
3.1 Algorithms:
3.1.1 Watermark embedding algorithm
I. The Singular Value Decomposition was applied on cover image ‘Ic’ and
watermark image on ‘Iw’ as follow:
𝐼𝑐 = 𝑈𝑐𝑆𝑐𝑉𝑐 𝑇 … … … … 𝑒𝑞. (4.1)
IC=UcScVcT ………….eq (4.2)
Iw=UwSwVW
II. Now Sw of watermark image Iw was added to Sc of cover image Ic as:
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Method-I Method-II
• Uwi= Uc
• Swi=(1-t)Sw+tSc
• Vwi=Vc
• Uwi=Uc
• T1=(1-cos(t*PI)/2
Swi=(1-t1) Sw+t1Sc
• Vwi=Vc
III. After this step, Now the watermarked image Iwi obtained by using Vwi,
Swi and Vwi as follow:
Iwi=UwiSwiVwi T……………………………..eq(4.3)
We also know that the watermarked imaged Iwi was also attacked by
different techniques like blurring, compression, filtering, addition of
noise etc.
3.1.2 Watermark extraction Algorithm
Given Iwi,Iw, t & Ic the embedded watermark was extracted as:
I. The SVD was performed on watermarked image Iwi and watermark
image Iw as
Iwi=Uwi Swi VwiT ………………………….…….eq (4.4)
Iw=Uw Sw VwT ………………………………....eq (4.5)
Ic=Uc Sc VcT …………………………………..eq (4.6)
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II. After this step now, singular values of extracted watermarked image Iew
is calculated as:
III. Now the watermarked image Iew was obtained by using Uew, Sew and
Vew as:
Iew=Uew Sew VewT…………………………………eq (4.7)
IV. Then the comparison between the original watermarked image Iw with
obtained watermark Iew image is performed by using different operations.
V. After this, the comparison parameters Accurate Rate (AR), Normalized
Correlation (NC), and Peak Signal to Noise Ratio (PSNR) were
calculated.
All these operations are performed for different values for t.
3.2 Research Model
The proposed methodology for this study is best shown in figure 4. The Cover image and
watermark images are decomposed into corresponding singular values. After
decomposition images, then interpolation method is applied to watermark the cover image.
Then the reverse SVD method is applied to produce the watermarked process. Different
attacks have been done over the watermarked image. After this process, we extract the
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watermark from the attacked watermarked image by using the SVD and reverse
interpolation technique, as best shown in the following flowchart.
Figure 2: Proposed watermarking Scheme
3.3 Programming Tools
I. Java programming environment
II. Matlab
III. JAMA Package
3.4 Working environment
• Dual core CPU 3.00 GHz or higher
• Operating System: Linux, Ubuntu, windows 8,7,64/32 bit
• Minimum 2 GB RAM or higher
• Minimum 50 GB HDD or higher
3.5 Technologies
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The following technologies have been used during the preceding of this
dissertation:
• Singular Value Decomposition (SVD)
• Interpolation Methods
• Linear Interpolation
• Cosine Interpolation
• Comparison metrics
• Peak Signal to Noise Ratio
(PSNR)
• Normalized Correlation
(NC)
• Accurate Rate (AR)
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Chapter 4
Performance Measures
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Performance Measures
The performance measurement of this study is the process of collecting,
analysis and reporting information related to the performance parameters of
an individual, in group, system or a component. Following comparison
metrics are used as:
• Accuracy Rate (AC)
• Normalized Correlation (NC)
• Execution time
• Peak signal to Noise Ratio (PSNR)
4.1 Normalized Correlation (NC)
The Normalized Correlation (NCC) calculates the similarity and dissimilarity
measurement between original watermark and extracted watermark, by using
this formula which is given as:
𝑵𝑪 = ∑
𝑁
𝑖=1
∑ 𝑤(𝒊, 𝒋) ∗ 𝑤′
𝑁
𝑗=1
(𝒊, 𝒋)
∑ ∑ 𝒘𝟐(𝒊, 𝒋)𝑵
𝒋=𝟏𝑵𝒊=𝟏 ……………………….eq (5.1)
In this equation, the N×N is the watermark size, w (i, j) and w’(i, j) represents
the watermark and recovered images watermark respectively.
4.2 Accuracy Rate
In this dissertation, the Accuracy Rate (AR) is used to measurement the
difference between the recovered image and watermark image. The Accuracy
Rate is calculated as follows:
AR= CP/NP ……………………………..eq (5.2)
In this above equation, the NP is the number of pixels in the original
watermark image and CP is the correct pixels obtained by comparing the
pixels of the covered watermark and corresponding original images.
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4.3 Peak Signal to Noise Ratio (PSNR)
The PSNR is an engineering term for the measurement of signal power. It is
measure the ratio between the maximum power of a signal and power of
corrupting noise that affects the quality of signals [21]. Because there are many
signals have a wide dynamic range, the PSNR is usually expressed in terms of
the log decibel scale. The PSNR is easily defined via mean squared error (MSE).
This formula is easily defined as follows.
….eq (5.3)
…….eq(5.4)
In this equation, MAXi is the maximum possible pixel value. PSNR is help to measure the
imperceptibility of the watermark
4.4 Execution Time
The time is very important part to execute the every interpolation method. The time is
calculated for each interpolation method. It is considered as performance measure parameters
as operation speed of any interpolation method which impacts the choice of the method.
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Chapter 5
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SYSTEM VALIDATION SCHEMES
The validation for the system is to validate the system for checking the Imperceptibility and
Robustness. There are two types of validation are given below.
• Imperceptibility Validation
• Robustness Validation
5.1 Imperceptibility Validation
Imperceptibility is the main characteristic of digital image watermarking. The
watermark data must be invisible in watermarked cover image. This validation process
is done by using given measures.
5.1.1 Normalized Correlation
The Normalized correlation calculate the similarity between the watermarked cover
image and original image. Normalized Correlation measure the pixel wise comparison
between watermarked image and original image.
Expected Output:
Normalized correlation give the satisfying output so that the information in cover
image is invisible.
5.1.2 Peak Signal-to-Noise Ratio
The PSNR is technical term, it calculate the ratio between the maximum power of
signal and the corrupting noise signal. The more PSNR, is more similarity the extracted
watermark image.
Expected Output:
PSNR measure the decibel value, it satisfying range of cover image so that it is invisible.
5.2 Robustness Validation
The Robustness Validation is another main characteristic of image watermarking. The
extracted information must be similar to the embedded original watermark image. The
Validation is done by using the given measures as.
5.2.1 Normalized Correlation
The Normalized correlation calculate the similarity measurement between the extracted
watermark image and original embedded watermark image. The NC measure the pixel wise
comparison between the original and watermark images.
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Expected output:
The correlation give the satisfy output so that the extracted watermark is recognizable.
5.2.2 Peak Signal-to-Noise Ratio
The PSNR is technical term, it calculate the ratio between the maximum power of
signal and the corrupting noise signal. The higher the value of PSNR, the similar extracted
watermark information in image, so that the more PSNR value, it is strong robustness in
image.
Expected output:
The correlation give the satisfy output so that the extracted watermark is recognizable.
5.2.3 Accuracy Rate
The Accuracy Rate (AR) give the difference between the recovered image and original
watermark image. It return the correct number of pixel which is count in total pixels.
Expected output:
The accuracy rate must satisfy the range of watermark image which is recognizable as
compared to the original one watermark image.
5.3 Computation time
The time complexity is very important in every process, so that time is measured for each
interpolation method. Time is measured only for interpolation technique as well as for the
corresponding in reverse process.
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Chapter 6
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OBSERVATIONS AND DISCUSSION
The discussions and results obtained from the interpolation operations are show in this
chapter. Details of the obtained results can be shown in appendix section of this dissertation.
Input data as cover image for observation, analyzing and discussion as follows:
a) Test picture for cover and watermark
i. Lena as cover image (low resolution)
ii. Cisco as watermark image (low resolution)
b) Real image for cover and watermark
i. Durbar as cover image (high resolution)
ii. Vanquish avatar as watermark image (high resolution)
Analysis are done on the basis of following factors:
6.1 On the basis of imperceptibility
The imperceptibility is important factor during the addition of watermark. This process is
done by using the following measure.
6.1.1 Obtained Data
I. NC Values
Figure 3: NC for test image [From table 1]
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Figure 4: NC for real image [From table 2]
II. PSNR Values
Figure 5: PSNR for test image [From table 3]
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Figure 6: PSNR for real image [From table 4]
6.1.2 Analysis
In this above results, we have found that the cosine method give the better NC and PSNR
values as compared to liner interpolation method. So Cosine interpolation method generate
better results than linear interpolation. It is also applicable for the lower weight of cover
image. In NC and PSNR values and result of watermarked image, we can say that this
method provides good imperceptibility for the cover image. The reason behind this is that
the Singular Value Decomposition method provides better stability to the system under the
decomposition. The cosine method also provides good approximation to the intermediate
values. Ultimate the required imperceptibility has achieved.
6.2 On the basis of Robustness
During the watermark extraction, the robustness factor is also important. Here NC and
PSNR values are calculated for different attacks. This is measured by following results.
6.2.1 Obtained data
I. NC Values
• Without any attack
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Figure 7: NC for test image [From table 5]
Figure 8: NC for real image [From table 6]
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• After adding noise
Figure 9: NC for test image [From table 7]
Figure 10: NC for real image [From table 8]
• After rotation
Figure 11: NC for test image [From table 9]
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Figure 12: NC for real image [From table 10]
• After negation
Figure 13: NC for test image [From table 11]
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Figure 14: NC for real image [From table 12]
II. PSNR Values
• Without any attack
Figure 15: PSNR for test image [From table 13]
Figure 16: PSNR for real image [From table 14]
• After adding noise
34
Figure 17: PSNR for test image [From table 15]
Figure 18: PSNR for real image [From table 16]
• After rotation
Figure 19: PSNR for test image [From table 17]
35
Figure 20: PSNR for real image [From table 18]
• After negation
Figure 21: PSNR for test image [From table 19]
Figure 22: PSNR for real image [From table 20]
36
6.2.2 Analysis
From the above graphs of NC and PSNR and extracted watermarked image snaps are included
in appendix, the following results are derived from the above graphs.
• When there is no attack on a watermarked cover image, the extracted is very good and very
similar to the original embedded watermark. This is reflected by corresponding PSNR and
NC values. In this type of case, the two interpolation methods generate similar PSNR and
NC results but the Cosine technique provide good PSNR values, as shown in above graphs.
• When we add salt and pepper noise on watermarked cover image, the extracted watermark
image is recognizable in range 25% to 75% of cover image weight. It is also reflected by
the corresponding values of NC & PSNR, these values are not good but visually extracted
watermark is recognizable. So in this case, the linear interpolation method generate better
NC values whereas the Cosine interpolation method generate good PSNR values.
• When we rotate watermarked cover image at 45 degree, the extracted watermark is good
and recognizable. So, the PSNR & NC values are no so good but visually the extracted
watermarked is recognizable, as shown in the images included in appendix. In this case, the
Cosine methods generate better PSNR & NC results as compared to other methods.
• When we take the negation of the watermarked cover image, the extracted watermark is
very good and better recognizable, although the corresponding PSNR & NC values are not
good. There is also reflected by the images in appendix section. So the negation of the image
has no much effect. All these methods provide similar measure of the performance
parameter, as reflected in above graphs.
In above results are get after implementation of proposed algorithm. The main property of
digital image watermarking is robustness & imperceptibility. This Singular Value
Decomposition (SVD) is based on watermarking technique is able to provide good
imperceptibility to the watermarked cover image but it is also unable to provide robustness
in some attacks. So the extracted watermarked is recognizable, the PSNR & NC values are
low but good in some cases.
6.3 On the basis of computation time
37
6.3.1 Obtained data
Figure 23: Computation time for test image [From table 21]
Figure 24: Computation time for real image [From table 22]
6.3.2 Analysis
The computation time for the both interpolation method are similar. So there are no distinction
in performance of those methods on basis of computation time for two interpolation methods.
But above graphs shown some sharp rise when time used up. It may be just when memory &
CPU of the computer busy on serving another process. Otherwise, both interpolation method
used computation time are similar.
38
CHAPTER 7
39
Conclusion and Recommendation
7.1 Conclusion
Digital image watermarking is the process of embedding the watermark image in the
cover image so that the embedded watermark image can be used to prove ownership and
copyright regarding issues. It has two main characteristics viz. imperceptibility and
robustness. Singular Value Decomposition (SVD) technique has characteristics to resist
changes on its singular values and make the system stable. This property of SVD is used
in this study. Two interpolation methods are used individually to mark the watermark in
the cover image. As we have observed and discussed, “SVD based digital image
watermarking using linear and cosine interpolation method” is very good in providing
imperceptibility to the watermarked cover image. The imperceptibility is possible
because of the stability property of SVD. Though the extracted watermark was good
enough to be recognized, the performance measuring parameters was not good enough
to be considered in some cases. Hence for the robustness, the SVD based image
watermarking needs another transformation before applying SVD decomposition. The
two interpolation methods used were linear and Cosine. The Cosine method provided
better result and parameter values as compared to other interpolation methods. Also time
required by all two methods was similar. Hence, Cosine interpolation method can be
used for adding watermark for better result.
7.2 Limitation of dissertation
• This study does not include for color images.
• The study deals only singular watermarking scheme.
• The targeted cover image file is of 512*512 resolution gray images and the
watermark is of 256*256 resolution gray images.
7.3 Future enhancement and important achievement
From by using this dissertation one can make the watermarking module. S/he can implement
watermarking method based on SVD and Cosine as it was justified in previous chapters. This
is the major achievement of this study. Anyone can improve this work by implementing
40
frequency domain transformations like Discrete Cosine Transform (DCT), Discrete Wavelet
Transform (DWT) for improving robustness of the process. S/he can study the same on color
image. S/he can improve it by including metrics like processor usage, memory usage etc. Also
other interpolation technique can be included.
41
APPENDIX A: Performance Parameters obtained
1. During watermark addition, for imperceptibility
i. NC Values:
Cover weight
Method
0.25 0.35 0.45 0.55 0.65 0.75
Linear 90.61 92.7 93.96 94.17 95.15 96.55
Cosine 89.16 90.3 92.4 94.45 96.14 97.66
Table 1: NC for test image
Table 2: NC for rea
ii. PSNR Values:
Table 3: PSNR for test image
Table 4: PSNR for
Cover weight
Method
0.25 0.35 0.45 0.55 0.65 0.75
Linear 74.61 77.7 81.96 84.17 87.15 91.55
Cosine 70.16 75.3 80.4 85.45 90.14 94.66
Cover weight
Method
0.25 0.35 0.45 0.55 0.65 0.75
Linear 22.61 23.7 25.96 27.17 29.15 31.55
Cosine 21.16 22.3 24.4 27.45 31.14 36.66
Cover weight
Method
0.25 0.35 0.45 0.55 0.65 0.75
Linear 16.61 17.14 19.18 20.03 22.09 25.77
Cosine 15.16 16.12 18.23 21.22 24.66 30.19
42
2. During watermark extraction, robustness
i. NC Values:
• Without any attack
Table 5: NC for tes
Table 6: NC for rea
• after noise addition
Table 7: NC for tes
Table 8: NC for rea
Cover weight
Method
0.25 0.35 0.45 0.55 0.65 0.75
Linear 98.65 98.23 98.21 98.88 98.45 98.17
Cosine 98.05 98.56 99.07 98.7 98.69 97.12
Cover weight
Method
0.25 0.35 0.45 0.55 0.65 0.75
Linear 99.12 99.01 98.13 98.54 98.14 98.03
Cosine 99.05 99.56 99.07 98.06 98.61 96.15
Cover weight
Method
0.25 0.35 0.45 0.55 0.65 0.75
Linear 55.01 55.04 54.14 53.54 53.41 52.03
Cosine 55.05 55.56 54.07 53.06 52.61 52.15
Cover weight
Method
0.25 0.35 0.45 0.55 0.65 0.75
Linear 71.01 69.04 67.14 64.54 62.41 60.03
Cosine 72.05 70.56 68.07 64.06 60.61 59.15
43
• After rotation
Table 9: NC for tes
Table 10: NC for r
• After negation
Table 11: NC for test image
Table 12: NC for r
Cover weight
Method
0.25 0.35 0.45 0.55 0.65 0.75
Linear 35.01 40.04 36.14 55.54 68.41 63.03
Cosine 31.05 36.56 36.17 58.05 72.18 57.15
Cover weight
Method
0.25 0.35 0.45 0.55 0.65 0.75
Linear 43.01 45.04 49.14 54.54 59.41 68.03
Cosine 41.05 43.56 47.17 55.05 66.18 57.15
Cover weight
Method
0.25 0.35 0.45 0.55 0.65 0.75
Linear 25.5 26.6 28.15 30.54 34.41 40.03
Cosine 24.05 26.56 28.17 31.05 39.18 56.15
Cover weight
Method
0.25 0.35 0.45 0.55 0.65 0.75
Linear 65.01 63.04 61.14 57.54 54.24 47.05
Cosine 66.05 63.56 62.17 57.05 48.18 36.15
44
ii. PSNR Values:
• Without any attack
Table 13: PSNR fo
Table 14: PSNR fo
• After noise addition
Table 15: PSNR for test image
Table 16: PSNR for real image
Cover weight
Method
0.25 0.35 0.45 0.55 0.65 0.75
Linear 23.01 27.04 31.14 37.54 37.24 35.05
Cosine 19.05 24.56 30.17 38.05 35.18 30.15
Cover weight
Method
0.25 0.35 0.45 0.55 0.65 0.75
Linear 24.01 30.04 38.14 34.54 30.24 28.05
Cosine 23.05 26.56 39.17 32.05 28.18 26.15
Cover weight
Method
0.25 0.35 0.45 0.55 0.65 0.75
Linear 5.01 5.04 5.14 5.54 5.24 5.05
Cosine 4.05 5.56 5.17 5.05 5.18 5.15
Cover weight
Method
0.25 0.35 0.45 0.55 0.65 0.75
Linear 7.02 7.04 7.24 7.5 7.25 7.09
Cosine 7.05 7.56 7.17 7.05 6.18 6.15
45
• After rotation
Table 17: PSNR for test image
Table 18: PSNR fo
• After negation
Table 19: PSNR for test ima
Table 20: PSNR fo
Cover weight
Method
0.25 0.35 0.45 0.55 0.65 0.75
Linear 4.01 4.04 3.14 7.54 9.24 6.05
Cosine 3.05 4.56 3.17 7.05 8.18 6.15
Cover weight
Method
0.25 0.35 0.45 0.55 0.65 0.75
Linear 5.01 5.04 5.14 6.54 7.24 7.06
Cosine 5.06 5.7 5.17 6.05 7.18 7.15
Cover weight
Method
0.25 0.35 0.45 0.55 0.65 0.75
Linear 8.01 8.04 8.14 7.54 7.24 6.05
Cosine 8.05 8.56 8.17 7.05 6.18 4.12
Cover weight
Method
0.25 0.35 0.45 0.55 0.65 0.75
Linear 2.01 3.05 3.15 3.55 3.24 4.05
Cosine 2.06 2.56 3.17 3.05 4.19 6.16
46
1. Execution time
Table 21: executio
Table 22: executio
Cover weight
Method
0.25 0.35 0.45 0.55 0.65 0.75
Linear 392 343 326 361 312 361
Cosine 329 344 345 312 314 343
Cover weight
Method
0.25 0.35 0.45 0.55 0.65 0.75
Linear 361 361 329 343 343 330
Cosine 404 343 343 312 327 327
47
Appendix B: Images Obtained
First the cover image is with watermark and the results are shown below.
48
Figure 25: Embedding watermark using SVD & interpolation
After embedding the watermark in the cover image, various attacks are applied in the
cover watermarked image. The results are shown below.
49
Fig 26: Extracted watermark at t=0.25 using Lena & Cisco
50
Fig 27: Extracted watermark at t=0.45 using Lena & Cisco
51
Fig 28: Extracted watermark at t=0.75 using Lena & Cisco
52
Fig 29: Extracted watermark at t=0.25 using Durbar & Vanquish
53
Fig 30: Extracted watermark at t=0.45 using Durbar & Vanquish
54
Fig 31: Extracted watermark at t=0.75 using Durbar & Vanquish
55
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