7

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.

9

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”.

22

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.

26

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.