Post on 22-Aug-2020
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CHAPTER 2
LITERATURE REVIEW
The appropriate background of literature and the concept of digital image
watermarking are reviewed in this chapter. The copyright protection of multimedia
content has become a critical issue now days due to easy copying, the latest
developments in digital transmission and widespread of broadband networks and the
internet [18]. The transmission of information takes place in different forms and is used
in many applications, where the communication must be done in secret form. Such secret
communication techniques include the transfer of medical data, bank transfers, corporate
communications, purchasing using bank cards, a large amount of information through
emails and etc. Steganography, cryptography and watermarking are the different
techniques used to perform secret communication.
N.Provos and P.Honeyman [26] said that steganography is entirely different from that
of cryptography and watermarking, even though all the techniques are used to hide the
information. Steganography hides the information, while cryptography provides
concealing for encoded information. Similar to steganography, watermarking is about
hiding information in other image, but the difference is that watermark must be somewhat
resilience against attempts to remove it. The information hiding technique can be
extended to protect the copyright of multimedia content. The watermarking and
steganography techniques can be used to protect copyright of information, and conceal
secrets.
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2.1 DIGITAL STEGANOGRAPHY
The word steganography is the combination of Greek words Steganos and graphy,
where Steganos means covered or protected, and the word graphy means writing or
drawing. Therefore, the steganography is defined as covered writing and is used to hide
information so that it cannot be detected during the transmission process through the
channel [27].The advantage of steganography is that messages do not attract the attention
of unauthorized users. Thus cryptography protects the content of a message, whereas
steganography protects both messages and communicating parties [23].
2.1.1 Properties of Steganography
All the steganographic algorithms need to fulfill the following basic requirements.
Invisibility- The first and foremost requirement of steganography algorithm is its
invisibility, so that it should not be noticed by the human eye.
Payload Capacity- Steganography requires sufficient embedding capacity because they
provide hidden communication.
Robustness against Stastical Attacks- Statistical analysis is the technique of detecting
hidden information from the image by applying different tests and performing different
attacks.
Independent of file format- The strength of steganographic algorithms lies in the ability to
embed information in any type of image file format.
2.1.2 Applications of Steganography
To have secure secret communication, where strong cryptography is not
possible.
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In military applications, where even the knowledge that two parties
communicate can be of large importance.
2.2 DIGITAL IMAGE WATERMARKING
The technology of digital image watermarking used to protect copyright of
authenticated users information by inserting watermark in the host image [6]. Based on
the robustness of the watermarks, watermarks are classified as robust watermarks, fragile
watermarks and semi-fragile watermarks. Robust watermarks can withstand different
malicious distortions, whereas fragile watermarks can easily be destroyed by normal
image attacks and semi-fragile watermarks can resist only minor changes and can easily
be destroyed by image distortions. The watermarks can also be classified as visible and
invisible based on perceptibility.
2.2.1 Properties of Digital Image Watermarking
The efficiency of a digital image watermarking process can be evaluated based
on the properties of imperceptibility, robustness, capacity, data payload,
fidelity, security, the cost of computation, recovery of watermark with or
without the need of the cover image and the speed of embedding process etc.
[27-29].
To understand watermarking methods and determine their applications, the following
properties of digital image watermarking must be known.
Robustness-of a watermark is its ability to withstand different image distortions
such as cropping, rotation, filtering, resizing and compression, etc.
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Data Payload- is the data size of the watermark in cover image and it depends on
the size [27].
Capacity- is defined as the amount of information that can be carried by
watermark. If more than one watermark embedded into cover image, the capacity
of the watermarked image equal to the sum of the information carried by
individual watermarks. If the robustness of the watermarked image increases, the
capacity also increases and the imperceptibility decreases, hence there is a
tradeoff between imperceptibility and robustness [19].
Imperceptibility– defined as the quality of the watermarked image that cannot be
destroyed by the watermark.
Fidelity- defined as the visual similarity between the cover image and the
watermarked image.
Security- of the watermark defined as its ability to resist different attacks, which
try to destroy the watermark and try to remove the watermark from the cover
image.
Computational cost-of the watermarking technique defends upon the resources
required to perform watermark embedding and extraction.
2.2.2 Applications of Digital Image Watermarks
Different applications of digital image watermarking are as follows:
Digital Rights Management (DRM)/Owner identification “can be defined as the
description, identification, trading, protecting, monitoring and tacking of all forms
of usages over tangible and intangible assets. It concerns the management of
digital rights and enforcement of rights digitally”.
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Copyright protection provides protection to the assets of authorized copyright
holders and prevents third parties from copying or claiming the ownership.
Robust watermarks used to protect the rights of the owners. It should be possible
to detect the watermark despite common image processing, geometrical
distortions, image compression, and many other image manipulations.
Authentication refers to the integrity assurance of the image and the applications
include the validation of digital artworks, cultural heritage paintings and medical
records.
Broadcast monitoring used to track the broadcast of a particular file over a
channel where watermarks embedded into advertisement sections.
Device control- in radio and television signal processing the embedded
watermarks can control the features of a receiver.
Medical Applications where the unique ID of the patient marked in X-ray film
references to monitor the flaws in the bones, etc.
Fingerprinting where information about the recipient of the digital media
conveyed by the watermarks in many applications.
Copy control where to protect the copyright of video content watermarks used to
control the functionality of a recorder.
“Robust watermarks can resist different image processing operations; hence they
are suitable for copyright protection. On the other hand, fragile watermarks can be
sensitive to change; hence they are best suited to tamper detection. Semi-fragile
watermarks can be used in some special cases of authentication and tamper
detection” [27].
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2.2.3 Key differences between watermarking and Steganography
Digital Image Watermarking
Inserts information related to either to the host signal or its owner.
Main goals of digital image watermarking are information authentication and
copyright protection.
It is either visible or imperceptible.
To communicate between a point and multiple points.
Capacity is not a major issue
Robustness is an important issue
Digital Steganography
Must not only be imperceptible but also statistically indictable.
For point-to-point communications.
The main goal of steganography is covert communication.
It can insert any kind of information.
Capacity is one of the important issues.
May or may not be robust.
2.3 DIGITAL IMAGE WATERMARKING ALGORITHMS
These algorithms classified into three categories namely spatial domain, feature
domain and transform domain methods. In the first method, the watermark inserted
directly into pixel values of the host image, whereas in feature domain methods, the
insertion of watermark depends upon the region, boundary and object characteristics. On
the other hand in the third method, the watermark is inserted into the host image‟s
transformed coefficients.
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2.3.1 Spatial Domain Techniques
The LSB insertion method, the patch work method and texture block coding method
are useful techniques in the spatial domain where the luminance and location of the
image pixels are processed directly [6]. The drawback of the least significant method is
that the lossy compression can easily destroy these bits. In general, the techniques that
modify the least significant bits are extremely sensitive to signal processing operations
and weak to watermark attacks. The contributing factor to this weakness is the fact that
the watermark must be invisible. As a result, the magnitude of the embedded noise can be
limited by the smooth regions of the image, which most easily exhibit the embedded
noise.
2.3.2 Transform Domain Techniques
Special transformations are used in transform domain to process the coefficients in
frequency domain to hide the data. Transform domain methods include “Discrete Cosine
Transform”,“CounterletTransform”,“DiscreteWaveletTransform”, “Curvelet Transform”,
“Fast Fourier Transform” etc. In these methods high and middle frequency coefficients of
the cover image will be selected to insert the watermark. The watermark does not inserted
into low frequency coefficients because they can be suppressed by filtering as noise [7].
The transform domain method provides more robustness to compression, filtering,
rotation, cropping and noise attacks compared to the spatial domain method.
In transform domain to embed a watermark, first transform is applied on the cover
image and then modifications are made to the transformed coefficients.
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Cox et al [32] find parallels between spread-spectrum communications and
watermarking and used a “frequency domain transform” to convert an image into another
domain.
In frequency domain, a sequence of values I0= I0[1], I0[2], ……I0[n] are extracted
from the given carrier image and then this sequence is modified as per the requirement.
The sequence of real numbers w = w[1], w[2] ...…w[n] represents the watermark.. Each
value of this watermark sequence is chosen independently from the Gaussian distribution
with zero mean and with variance unity.
Three different formulas to embed watermark, whose difference lies in their
embedding characteristics and in their invertibility are given below:
Iw[i] = I[i] +αw[i] ………………………… (2.1)
Iw[i] = I[i] (1+αw[i]) ……………………… (2.2)
Iw[i] = I[i] +exp (αw[i]) ……………………. (2.3)
Where α is the scaling or watermark strength parameter, which influences the
robustness and the fidelity of the watermarked image.
Watermarking can be implemented in frequency domain, such as proposed by Cox et
al [32], where the embedding technique is based on DCT and Pseudo Noise sequence.
The extraction of watermark depends on the knowledge of the cover image and the
frequency locations. The normalized correlation coefficient is computed and set to a
certain threshold. If the normalized correlation coefficient is large enough, the watermark
is detected. This Cox et al method is robust to image scaling, JPEG compression,
dithering, cropping, and rescanning.
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Another watermarking scheme in frequency domain is wavelet transform technique.
Barni et al [33], proposed a “watermarking method on decomposition of wavelet
transforms. This technique decomposes the cover image into low and high frequency
coefficients with different orientations and DWT is applied to the cover image”. The
watermark is inserted into the highest level sub bands as per following rule:
IwLH
[i,j]= I0LH
[i,j]+αβLH
[i,j]w[iN+j]………..(2.4)
IwHH
[i,j]= I0HH
[i,j]+αβHH
[i,j]w[MN+iN+j] ……(2.5)
IwHH
[i,j]= I0HH
[i,j]+αβHH
[i,j]w[2MN+iN+j]……….(2.6)
Where α is the global parameter for watermarking strength, βis the local weighting
factor and w is the pseudo random binary sequence. The masking characteristics of the
human visual system depend on this local weighting factor and the watermark sequence
is computed to retrieve the watermark.
Fractal watermarking schemes are based on fractal compression, which is developed
based on iterated function systems. The fractal encoding algorithm partitions the original
image into non- overlapping domain cells. The image is covered with overlapping
domain cells. For each range cell, the corresponding domain cell and transform are
searched to determine the best cell range. This step is computationally expensive. The
range of transforms typically includes affine transforms, the change of brightness and
contrast. This transform describes the self-similarity between the range cell and the
corresponding domain cell. To retrieve the watermark from the watermarked block, the
corresponding domain cells reveal the embedding information.
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Samesh Oueslati et al, [13] proposed “an adaptive image watermarking scheme. In
this method, neural networks are used to automatically control and maximum image–
adaptive strength watermark”.
Cheng et al, [16] developed “a blind watermarking algorithm based on the human
visual system and radial basis function neural network for digital images. In this method,
RBF is implemented to embed and extract the watermark from the host image”.
Nizar Sakr et al, [20] developed “an adaptive wavelet-based watermarking algorithm
that is based on the model of DFIS and HVS. In this method; Sugeno-type fuzzy model is
used to find a valid approximation of the quantization step of image coefficients and the
HVS properties are modeled by using biorthogonal wavelets to improve watermark
robustness and imperceptibility”.
Wu Bo XiaoMing et al, [21] developed “digital image watermarking encryption
algorithm using fractional Fourier transform, which is robust against JPEG compression
and Gaussian low-pass filtering”.
Alain Tremeau and Damien Muslet [38] explained in detail about recent trends in color
image watermarking. Teruya Minamoto and Kentaro Aoki [39] proposed “a blind digital
image watermarking method using interval wavelet decomposition”.
FENG Yang, LUO Senlin, PAN Limin [40] proposed “an extensive method to detect
the image digital watermarking based on the known template”. This method is used to
extract some special features from DWT, DCT and spatial domains of the template and
image. Then these features are used to detect the watermark.
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Yu Chang et al, [41] developed “the digital image watermarking technology based on
neural networks”. In this method they proposed three stage watermarking technique to
improve the robustness of the watermarked image.
Jing-Jing Jiang et al, [42] proposed “digital image watermarking based on patchwork
and radial basis neural network. In this method two special subsets of the cover image
features are selected embed watermark”. One subset is used to add a small constant while
the other is used to subtract the same from other patch.
Xinhong et al, [43] developed “a blind watermarking algorithm based on neural
network. In this method Hopfield Network and the Noise Visibility Function are used for
adaptive watermark embedding”.
Quan Liu and Xumei Jiang [44] proposed “design and realization of a meaningful
digital watermarking algorithm based on RBF neural network. In this method, the radial
basis function network and discrete cosine transform are used to simulate human visual
specialty to determine the intensity of watermark embedding”.
Chuan-Yu Chang et al, [45] proposed “robust digital audio watermarking in DWT
domain using counter-propagation neural network. In this method, the db4 filter of the
Daubechies wavelet is applied to decompose the coefficients of the host image to
improve the robustness”.
Ju-Liu, Xingang, Montse Najar and Miguel Angel lagunas [46] proposed “the robust
digital watermarking scheme based on ICA. In this method, the combination of DCT and
ICA is applied to improve the robustness”.
Cong Jin et al, [47] developed “an adaptive digital image watermark scheme based on
fuzzy set theory to get rid of the slow training speed network parameter sensibility”.
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Fan Zhang and Hongbin Zhang [48] proposed “different applications of neural
network to improve the watermarking capacity. In this method, a blind watermarking
based on Hopfield network is proposed to improve the robustness”.
Ahmad R Naghsh et al, [49] developed “robust digital image watermarking technique
based on neural network and DCT. In this method FCN is implemented to simulate the
visual and perceptual characteristics of the host image”.
Qun- ting Yang et al, [50] proposed “a novel robust watermarking scheme based on
neural network. In their method three identical watermarks are embedded into the low
frequency sub bands of the cover image to improve the performance of the watermarked
image in terms of robustness and imperceptibility”.
Song Huang et al, [51] developed “a blind watermarking technique with neural
network and HVS to improve the strength of the watermark”.
Santi P et al, [52] proposed “a new model of watermarking using spread spectrum to
reduce the bit error rate at the expense of computational complexity”.
Mukesh C et al, [53], proposed “a new method using HVS model for perceptual
masking with brightness, and sensitivity and texture as input variables to fuzzy system”.
Gursharajeet Singh Karla et al [54] developed “an algorithm based on properties of
random sequence generated by Chaos and Arnold transformations for robust digital
image watermarking”.
Mukesh Motwani, Nikhil Beke et al, [55] developed “an adaptive algorithm for 3D
models, which is robust to different noise attacks”.
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Shaowei Xia ET AL, [56] developed “robust digital image zero-watermarking
algorithm based on CDMA Technology, which has good performance under multiple
attacks”.
Pankaj U Lande et al [57], proposed “an image adaptive watermarking using
fuzzylogic. In this work they developed a low cost robust watermarking hardware based
on FPGA”.
Soheila Kiani and Mohsen Ebrahimi Moghaddam [58] developed “fractal based
watermarking techniques using fuzzy C-Mean clustering, which is robust against JPEG
compression, median filtering and additive noise”.
Said E. El-Khamy et al [59] proposed “a new algorithm where the host image
decomposed into DCT blocks, then classified using adaptive fuzzy classification and
perceptually embedded into each block to increase robustness against attacks”.
Jianzhen Wu et al, [60] developed “an adaptive watermarking algorithm to improve the
robustness of the watermarked image”.
Reza Mortezaei et al [61] proposed “a new watermarking technique using DFIS and
DCT which is robust against different attacks”.
LI Li Zong and Gao Tie gang [62] proposed “a new technique using DFIS and ART
for authentication”.
Nizar Sakr et al, [63] developed “an adaptive image watermarking techniques using
DFIS. This algorithm utilizes HVS model to improve the robustness”.
Hung-Jen et al, [64] developed “a watermarking techniquewith fuzzy ART to protect
the intellectual property, which is robust to internal attacks, geometric distortions and
image processing attacks”.
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Mingyan Zhang et al, [65] developed “a weighted recovery to insert a visually
recognizable image to improve the strength of the watermarked image against normal
lossy compression attacks”.
Ming-Shing Hsieh [66] developed “an image watermarking based on fuzzy inference
filter to provide transparency and robustness”.
Hai-Yan Tu Jiu-Lun Fan et al [67] presented “a robust watermark technique using the
Ridge let transform and fuzzy C-means to obtain a sparse representation for straight edge
singularity. In this method, FCM clustering is applied to classify the image pieces into
frat regions and texture regions adaptively”.
Prof.Sharvari C.Tamane and Dr.R.R. Manza [68] proposed “3D Models watermarking
using fuzzy logic using HVS in wavelet domain to improve the robustness”.
Hsiang-Cheh Huang et al, [69] developed “a fuzzy-based bacterial foraging algorithm
to design an effective fitness function to improve the quality and robustness of the
watermarked image”.
Lei Li et al, [70] proposed “a new technique, where the image is divided into weak
texture and strong texture blocks and watermark is embedded into strong structure blocks
to improve the robustness”.
Jun Fan, Yiquan Wu [71] developed “a watermarking technique based on fuzzy
clustering and SVD determine the strength of the watermark”.
Jiying Zhao et al, [72] proposed “a dynamic fuzzy logic approach using HVS to
provide a more robust and imperceptible watermark”.
Hajime Nobuhara et al, [73] presented “digital watermarking algorithm using an
image compression method based on relational equation to improve the imperceptibility
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of the watermarked image. In this method, image compression and reconstruction is done
on 100 images and confirmed that the signed image is distinguishable from the unsigned
image”.
Glumov et al [74] developed “a new block wise algorithm for large scale images. In
this method, the host image is divided into non-overlapping fragments and the average
centered magnitude spectrum is calculated for the entire host image to provide better
robustness”.
Farooq Husain, Ekram Khan and Omar Farooq [75] proposed “DFRFT-domain digital
image watermarking. In this method randomly distributed sequence is used as a
watermark to modify discrete fractional Fourier transform coefficients of the cover
image”.
Jindong Xu, Huimin Pang, Jianping Zhao [76], developed “a digital image
watermarking algorithm based on fast curvelet transform. In this method, the carrier
image decomposed by fast curvelet transform and the watermarked image scrambled by
Arnold transform”.
Mahasweta J.Joshi et al, [77] proposed “digital image watermarking in DCT-DWT
domain to protect watermarked images from illegal manipulations. This algorithm is
robust against white noise, Gaussian filtering and sharpening filter attacks”.
J.Anitha et al, [78] developed “a color image digital watermarking scheme using
SOFM based on codebook partition technique to embed the watermark bit sequence in
the vector quantization encoded blocks, which are robust against compression”.
G.Thirugnanam and S.Arulselvi [79] developed “a new watermarking technique to
provide high a peak signal to noise ratio compared wavelet transforms”.
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Hanjie Ji et al, [80] developed “a new watermarking technique using RST to protect
the watermarked images against geometric image manipulations with good robustness”.
Mohammadreza Ghaderpanah and A.amza [81] presented “a nonnegative matrix
factorization scheme for digital image watermarking to improve the performance of the
data embedding system and resist a variety of intentional attacks and normal visual
processes”.
Ming-Shing Hsieh et al [82] proposed “a technique to hide digital watermarks using
Multiresolution wavelet transform. In this method, the watermark can be detected by
comparing an experimental threshold with extracted values. They also proposed a multi
energy watermarking scheme based on a qualified significant wavelet tree to improve the
robustness of the watermarked images”.
A.N. Skodras et al [83] developed “robust digital image watermarking based on
chaotic mapping and discrete cosine transform to protect the watermarked images against
noise addition, filtering, JPEG compression and geometric manipulations”.
Naghsh-Nilchi et al, [84] proposed “robust digital image watermarking based on Joint
DWT-DCT technique to provide higher robustness noise attacks and enhancement”.
Po-Chyi Su and et al, [85] developed “wavelet- based digital image blind
watermarking which is robust against signal processing attacks and compression. In this
method blind watermark retrieval technique used to detect the embedded watermark
without the need of the original image”.
Bum-Soo Kim et al [86] proposed “a robust digital image watermarking method
against geometrical attacks by improving Fourier Mellin transform based watermarking.
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This method modifies and reorders function blocks of Fourier Mellin transform by the
use of an invariant centroid as the origin”.
Kilari Veeraswamy, B.Chandra Mohan et al, [87] developed “an image compression
and watermarking scheme using scalar quantization and counterlet transform with a
double filter bank structure based on the Laplacian Pyramid”. This method is superior to
wavelet transform method when the image contains more contours and is robust to
normal image attacks.
B.chandra Mohan et al, [88] implemented “a robust digital image watermarking
scheme using counterlet transform with multiple descriptions and quantization index
modulation”.
B.N.Chettarji et al, [89] developed “a robust digital image watermarking algorithm
based on singular value decomposition, dither quantization and edge detection which is
resilience to image attacks”.
Srinivas Kumar.S et al, [90] proposed “a robust multiple image watermarking scheme
using DCT with multiple descriptions, which is robust to local and global attacks”.
Blasubramanian Raman et al [91] implemented “real coded genetic algorithm based
stereo image watermarking in discrete wavelet transform domain. In this method a pair of
stereo images used to generate a disparity-image watermark to embed into the degraded
cover image by modifying singular values”.
Fouad Khelifi and Jianmin Jiang [92] developed “perceptual image hashing based on
virtual watermark detection to provide better robustness against geometric attacks and
image processing manipulations”.
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Sanjay N.Talbar et al [93] developed “hardware for FPGA prototype of robust
watermarking JPEG 2000 encoder, which is robust against scaling, rotation and most of
the geometric attacks”.
G.N.Shinde et al [94] proposed “fuzzy logic approach to encrypt watermark for still
images in wavelet domain based on FPGA. This watermarking system implemented by
hardware to meet real time constraints related to robustness and imperceptibility”.
Hanaa A.Abdallah et al [95] developed “blind wavelet-based image watermarking to
insert the watermark bits into the coarsest scale wavelet coefficients by performing three-
level wavelet decomposition”.
Gaurav Bhatnagar et al [96] proposed “DWT-SVD based dual watermarking scheme
to improve the protection and robustness by embedding dual watermarks into the cover
image. In this method the secondary watermark is easily detected but the primary
watermark is severely distorted”.
Hamed Modaghegh et al [97] developed “a new adjustable blind watermarking based
on GA and SVD considering image complexity and robustness. This algorithm is an
adjustable solution by changing the fitness function so that watermarking technique can
be converted into robust, fragile or semi-fragile types”.
Mohammad Reza Soheili [98] presented “a blind wavelet based logo watermarking to
resist cropping. In this method a binary logo is embedded into LL2 subband of the cover
image using quantization. The robustness of algorithm can be increased by adding two
dimensional parity bits to the binary logo”.
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Juan R. Hernandez, et al [99] proposed “watermarking techniques in DCT-domain for
still images. In this method spread spectrum technique is implemented in DCT domain to
increase the robustness and imperceptibility of the watermarked image”.
Samira Mabtoul et al, [100] developed “robust semi-blind digital image watermarking
technique in DT-CWT domain to increase the security of the watermarked image. In this
method two chaotic maps are generated and one is used to determine the blocks of the
cover image to embed watermark, while the other is used to encrypt the watermarked
image”.
S.Saryazdi, H.Nezamabadi-pour, and A.Hakimi [101] proposed “a blind watermarking
scheme for binary image authentication to detect any alterations. In this method the
binary host image divided into 2x2 sub-blocks and the last pixel is predicted from its
neighbors”.
Xiang-Wei Zhu [102] developed “blind watermark detection algorithm based on
generalized Gaussian distribution to protect copyright, intellectual and material rights of
distributors, authors and buyers. In this method a blind watermark detection technique is
developed according to the method of maximum likelihood estimation and the algorithm
is very much effective against most of the image attacks”.
Pik Wah chan et al,[103] proposed “a new technique for hybrid digital video
watermarking based on scene change analysis and error correction code, which is robust
against the attacks such as frame dropping, stastical analysis and averaging”.
Chih-Wei tang et al, [104] developed “a feature-based robust digital image
watermarking scheme using image normalization and Mexican Hat wavelet interaction.
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This scheme can survive low quality JPEG compression, sharpening, median filtering,
color reduction, cropping and rotation attacks”.
Chin-Chen Chang et al, [105] implemented “a new public-key oblivious fragile
watermarking for image authentication using discrete cosine transform to improve the
vulnerability to different image attacks”.
Sudip ghosh, Pranab Ray et al, [106] proposed “spread spectrum image watermarking
with digital design for greater robustness. In this method Field Programmable Gate Array
has been developed using VLSI and the circuit is integrated into the existing digital still
camera framework”.
Joachim J.Eggers and Bernd Girod [107] developed “blind watermarking to prevent
image manipulations and fraudulent use of modified images. In this method quantization
and scalar costa scheme are used to develop blind watermarking”.
Slaven Marusic et al, [108] presented “a detailed study of biorthogonal wavelets in
digital watermarking. In this paper they derived biorthogonal wavelet coefficients using
Cohen-Daubechies- Feauveau (CDF) biorthogonal wavelet system”.
2.3.2.1 Nagaraj.V.darwadkar Method
Nagaraj V. Dharwadkar et al [6], proposed “a non-blind watermarking scheme
using DWT-SVD to embed watermark singular values in the host image, which are very
difficult to remove or destroy” [6].
a. Watermark embedding procedure
Step 1: Read the color image I of size NxN.
Step 2: Read the monochrome image X of size MxM and apply DWT on X to
get D= {dij} of size MxM.
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Step 3: Compute Red(R), Green (G) and Blue(B) channels of the color image I
of size NxN.
Step 4: Transform R, G and B channels into Y, I and Q channels of the color
image.
Step 5: Compute third level DWT on Y channel to get the frequency
components {HH1, HL1, LH1, {HH2, HL2, LH2, {HH3, HL3, LH3}}}.
Step 6: Embed the watermark frequency coefficients, starting from HH1 for
each row select the frequency coefficients in descending order with respect
their absolute values.
Step 7: Modify each frequency coefficient f of cover image to ij.
If the subcomponent HH1 is insufficient to embed the complete watermark,
then insert in the other coefficients in the order {HL1, LH1, {HH2, HL2, LH2,
{HH3, HL3, LH3}}}.
Step 8: Save the location of the modified frequency coefficients into a key
array K of size NxN. The key array consists of value 1 if the coefficient is
modified otherwise 0.
Step 9: Replace by in decomposed y channel and compute inverse DWT
of modified Y channel.
Step 10: combine modified Y channel with I and Q to get watermarked
image .
b. Watermark extraction procedure
Step 1: Read the watermarked image of size of size NxN.
Step 2: compute , ‟ and channels of the watermarked image.
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Step 3: Transform these , and channels into , and channels.
Step 4: Compute third level DWT on channel to get the frequency
components {HH1, HL1, LH1, {{HH2, HL2, LH2, {{{HH3, HL3, LH3}}}}.
Step 5: Compute third level DWT on Y channel of the un-watermarked image
to get the frequency components {HH1, HL1, LH1, {HH2, HL2, LH2, {HH3,
HL3, LH3}}}.
Step 6: Extract the watermark bits from the frequency subcomponents using
the key array K as ij= ( - )/α. If ij˃ T, then ij =1 other wise ij =
0.Whwere i= 1, 2, 3…M and j= 1, 2, 3…M.
2.3.2.2 Yanhong Zhang Method
Yanhong Zhang [7] proposed “a blind watermark embedding/extracting algorithm
using RBF neural network. In this method, the blocking phenomenon problems in DCT
are overcome by using DWT. In this method, the original image decomposed into levels
using subband coding. When embedding the watermark, a secret key is generated to
identify the watermark beginning position, and after that, the secret key is used embed
and extract the watermark by using the trained Radial Basis Function Neural Network”
[7].
a. Watermark embedding procedure
Step 1: Transform the original image using DWT as is the LH4, HL4, HH4
sub-band coefficient.
Step 2: Use the secret key to select the beginning position to embed watermark
coefficient .
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Step 3: Quantize the coefficient of DWT, (i+key) by Q, as the input to the
RBFN and then get the output ( )
.
Step 4: Embed the watermark according to the following equation
( (
)) ; Where is the watermark sequence, q
is quantization value and is the coefficient of the watermarked image.
Step 5: Perform IDWT to get the watermarked image.
b. Watermark extraction procedure
Step 1: Use DWT to transform the coefficient as with the sub band
coefficients LH4, HL4, HH4.
Step 2: Quantize the DWT coefficient by Q as the input to the RBFN
and then get the output .
Step 3: Extract the watermark using the following formula
(
) .
Step 4: Measure the similarity of the extracted watermark and the original
watermark using the equation
√
Step 5: Use and threshold to judge if there is an embedded
watermark or not. If is larger than the threshold and the location is
equal to key, the watermark is affirmed.
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2.3.2.3. He Xu, Chang Shujuan Method
He Xu, Chang Shujuan [10], proposed “an adaptive image watermarking algorithm
based on neural network using DWT and DCT, where the ability of attracting is
improved by pretreatment and retreatment of image scrambling and Hopfield network”
[10].
a. Watermark embedding procedure
Step 1: The watermarking signal is applied as the training signal input to the
Hopfield network in order to finish the storage of the watermark.
Step 2: After doing scrambling transform, the watermark signal R is generated.
The affine transform is used as scrambling transform, the key is scrambling
times, and then the watermark pretreatment is completed.
Step 3: The low frequency sub- image LL is extracted from the original image
by using the first order DWT transform. I will be gotten by DCT transform
which process 8x8 block partitioning.
Step 4: The scrambling watermark sequence is embedded in high-frequency
coefficients of the image I according to the equation in order to
get . Where is embedding strength in the range 0 1.
Step 5: The IDCT is performed to get the low frequency sub-image LL which
contains watermark and IDWT is performed to get the watermark image.
b. Watermark extraction procedure
Step 1: The detected image and original image are processed by first order
DWT and T and I are gotten through DCT blocking phenomenon.
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Step 2: Watermark is extracted through T and I input watermark detection
module.
Step 3: The extracted watermark signal R is processed according to key inverse
scrambling to get the watermark.
Step 4: The extracted watermark is applied as input to the Hopfield network
and after data processing the watermark is extracted.
2.3.2.4. Charu Agarwal et al Method
Charu Agarwal et al, [34] developed “a new digital image watermarking in
DCT domain using FIS and HVS to provide better robustness” [34].
a. Watermark embedding procedure
Step 1: The cover image is divided into 8x8 blocks in spatial domain DCT is
computed on all blocks.
Step 2: Compute edge sensitivity (threshold), luminance sensitivity and
contrast sensitivity (variance) of all blocks of cover image.
Step 3: Supply these threshold, variance parameters as input to fuzzy inference
system.
Step 4: Apply fuzzy inference rules to the fuzzy inference system and obtain
the watermark weighting factor.
Step 5: Perform watermark embedding in low frequency DCT coefficients of
cover image.
Step 6: Compute the IDCT to obtain the watermarked image.
b. Watermark extraction procedure
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Step 1: Compute DCT of all 8x8 blocks of cover and watermarked (signed)
images.
Step 2: Subtract the computed coefficients of original image from watermarked
image.
Step 3: Recover the watermark using fuzzy inference system.
Step 4: Compare the recovered watermark with the original watermark using
Sim(X, X*) parameter.
2.3.2.5. Sameh Oueslati et al Fuzzy Method
Samesh Oueslati et al [35], proposed “a fuzzy watermarking system using the wavelet
technique for medical images. In this method, an adaptive watermarking algorithm
performed in the wavelet domain is proposed which exploits a human visual system
(HVS) and a fuzzy inference system (FIS). HVS is adopted to further ensure watermark
invisibility. The optimum watermark weighting function generated by using FIS and that
enable the embedding of maximum energy and imperceptible watermark” [35].
a. Watermark embedding procedure
Step 1: Input the cover image and watermark image.
Step 2: Convert the watermark into a stream of binary data consisting of zeros
and ones.
Step 3: Decompose the host image using Haar wavelet transform.
Step 4: Insert the data into wavelet coefficients, which have the largest values
in middle frequency coefficients.
Step 5: Perform the inverse Haar wavelet transform to get the watermarked
image.
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Step 6: Display the watermarked image.
b. Watermark extraction procedure
Step 1: Input the watermarked image.
Step 2: Decompose the watermarked image using Haar wavelet transform.
Step 3: Select the wavelet coefficients which have the largest values in the
middle frequency sub band.
Step 4: Compare the coefficients of the cover image and the watermarked
image depending upon the location.
If the coefficient of embedding˃ the original coefficient, then the data store in
it is 1
If the coefficient of embedding˂ = the original coefficient, then the data store
in it is 0
Step 5: Display the recovered image.
2.3.2.6. Ming-Shing Hsieh Method
Ming- Shing Hsieh [36] developed “DWT-based watermarking technique is proposed
to embed signatures in images to attest the owner identification and discourage
unauthorized copying” [36].
a. Watermark embedding procedure
Step 1: Sort the grey levels of watermark of size „n‟ in ascending order to
generate the sorted watermark .
Step 2: Decompose the host image into three levels with ten subbands of
wavelet pyramid structure and choose a subband (HL3) to embed watermark.
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Step 3: Calculate the weighted entropy of coefficients.
Step 4: Let the preset interval be and let„t‟ be the number of referenced
coefficients used as a key to extract watermark without the host
image. Coefficients with larger entropy are chosen from
subband Where . The larger entropy coefficients make the
watermark more robust and transparent. If
then
otherwise
(
) Where is used to get an integer part of its argument. Let
{ } be the set of referenced coefficients and the coefficients to be embedded
watermarks; { } is called the alternative coefficients. Sorting { } to
generate { } called the sorted alternative coefficients.
Step 5: Quantize { } using a preset interval, which will extract the
watermark W without the cover image.
Step 6: Embed watermark SW into subband HL3 using the equation
, To+T1+T2)/3=EnixT1.
Step 7: Save the symbol of embedded subband and perform IDWT to get the
watermarked image.
b. Watermark extraction procedure
Step 1: Decompose watermarked image into three levels with ten subbands
using DWT.
Step 2: Restore the scaling factor vi the symbol of embedded subband, symbol
map of SCi, corresponsive map of Ci and SCi and corresponsive map of Wi and
SWi.
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Step 3: Extract the sorted watermarks by the proposed extracting watermarking
algorithm.
Step 4: Rearrange the watermarks from corresponsive map of Wi and SWi to get
the extracted watermark.
2.3.2.7. Soheila et al Method
Soheila Kiani et al [37], proposed “Fractal based digital image watermarking using
fuzzy C-mean clustering, where a new watermarking method is used to embed a binary
watermark in to an image”.
a. Watermark embedding procedure
Step 1: The fractal encoding is applied to the original image to produce fractal
codes for all range blocks.
Step 2: Apply the fuzzy C-mean clustering on all the blocks and classify them into
four groups.
Step 3: As per centers calculated in previous step determine class A and B.
Step 4: For each bit of watermark fractal decoding process is used to construct
watermarked images.
b. Watermark extraction procedure
Step 1: Fractal coding is performed on watermarked images to generate fractal
codes of all range blocks.
Step 2: The fuzzy C-mean clustering is applied on all blocks to classify them into
four classes.
Step 3: According to the clusters class A and class B are determined.
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Step 4: For all range blocks the watermark bits are determined according to secret
key
Step 5: Perform step 4 on all bits of watermarked image according to the secrete
key.
These features have motivated to develop two new methods for watermarking in
transform domain using Back Propagation Neural Network (BPNN) and Dynamic Fuzzy
Inference System (DFIS).
2.4 RESEARCH OBJECTIVES
The objectives of this research work are as follows:
1. To explore digital image watermarking techniques using Back Propagation Neural
Network and Dynamic Fuzzy Inference System in Discrete Wavelet Transform
domain.
2. To develop watermarking techniques, which are imperceptible for an
unauthorized user, without affecting the original image quality.
3. To develop semi blind watermarking techniques so that the watermark can be
detected without the original image.
4. To develop watermark techniques, which are robust against cropping,
salt&pepper noise, rotation, JPEG compression, etc., and having supremacy over
existing watermarking methods.
2.5 PROBLEM STATEMENT
From the literature review, it is apparent that the digital image watermarking can be
achieved by using either embedding the watermark directly into the image pixels of the
cover image or into the transformed coefficients of the cover image. Creating the robust
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and semi blind digital image watermarking methods is still a challenging task for
researchers. These algorithms are robust against some attacks but not against most of
them. Also, some of the current methods are designed to suit only specific application,
which limits their widespread use.
To enable copyright protection and authentication, the robust digital image
watermarking scheme using Back Propagation Neural Network in DWT domain is
proposed, in which the geometrical effects such as cropping and rotation are minimized.
The main advantage of Back Propagation Neural Network is that it has good nonlinear
approximation ability and can establish the relationship between original image
coefficients and watermarked image coefficients by adjusting the network bias and
adjusting the weights between the layers before and after embedding the watermark. The
neural network method allows extracting watermark without the original image and thus
reducing the limitations in watermarking practical applications. The correlation
coefficient is further improved by using Dynamic Fuzzy Inference System. The Mamdani
type DFIS model is exploited in this DFIS method in order to determine a valid
approximation of each DWT coefficient using quantization. Furthermore, biorthogonal
wavelets are used to model HVS properties to improve watermark imperceptibility and
robustness. Finally, the results of both the methods are compared.
2.6 CHAPTER SUMMARY
This chapter presented an overview of digital image watermarking. A survey is made
on digital image watermarking and its limitations are also presented. Different domains
of watermarking are explained in the next chapter.