Optimized Digital Image Watermarking for Uncorrelated ... · Optimized Digital Image Watermarking for Uncorrelated Color Space ... Iwouldliketoexpressheremysinceregratitudetomysupervisors,Dr.

Optimized Digital Image Watermarking for

Uncorrelated Color Space

A Thesis

submitted in partial fulfillment of the requirements for the award of

the degree of

Doctor of Philosophy

by

Manish GuptaEnrolment No.: 11E7UCPEM4XP900

Supervisor(s):

Dr. Rajeev Gupta

Dr. Girish Parmar

Department of Electronics Engineering

Rajasthan Technical University, KotaRajasthan, India

December, 2015

c©RAJASTHAN TECHNICAL UNIVERSITY, KOTA, 2015

ALL RIGHTS RESERVED

Dedicated to

Lord Shri Goverdhan Maharaj Ji,

Lord Shri Banke Bihari Lal Ji,

My Father Shri Kalika Prasad Gupta,

Mother Smt. Rajkumari Gupta,

Brother Neeraj,

Wife Ruchi

and

Daughter Khyati & Son Anant

Acknowledgements

I would like to express here my sincere gratitude to my supervisors, Dr.

Rajeev Gupta, Professor, RTU, Kota and Dr. Girish Parmar,

Associate Professor, RTU, Kota, whose precious advice and friendly

encouragement made this work go smoothly throughout the period of

research. They are the source of never-ending inspiration for me. I

have been extremely lucky to have them as my mentors. Their precise

and lucid thoughts have helped me to manage many responsibilities

comfortably. They not only encouraged me to work but also gave

his guidance, experience, constructive thoughts and took keen interest

throughout the course of work and preparation of manuscript which

had made me worth that I claim to be now. Working with them made

me more independent and liable, which I believe is the most important

aspect in anyone’s career. I am indebted to them more than they

recognizes.

No appropriate words could be traced in the presently available lexicon

to avouch the excellent guidance given by Prof. R. S. Meena, Head,

Electronics Engineering Department at RTU, Kota, Prof. Mithilesh

Kumar, Principal, GEC, Jhalawar (Raj.), Prof. Ranjan Maheshwari,

Prof. Lokesh Tharani, Prof. Jankiballabh Sharma, Prof. Pankaj

Shukla, and Prof. Praveen Kumar. It is my great privilege to mention

the entire official staff for helping me in the official documentation

work right from the time of my registration into Ph.D. Moreover, I

would like to thanks to my research committee members and faculty

members of the institute for their help and support.

I am extremely thankful to Dr. Mukesh Sarsawat who has given his

constant guidance, support and encouragement throughout this study.

His expertise, experience, vigorous efforts and co-operation had made

me to complete this work. Without his unfailing support and belief in

me, this thesis would not have been possible.

I am extremely grateful to Hindustan Institute of Technology and

Management, Agra administration, Dr. A. K. Gupta, Dr. Shailen-

dra Singh, Dr. N. P. Singh, Mr. Jayash Kumar Sharma, Mr. Santosh

viii

Kumar Dwivedi, Mr. Kamal Karakoti, Mr. Viresh Chauhan and Mr.

Sudhir Verma for their cooperation during my stay at RTU, Kota.

I express my gratitude to Prof. Rajiv Saxena and Prof. Dinesh Chan-

dra for their constructive suggestions offered to me for successful com-

pletion of my thesis.

I am sincerely thankful to my friends Mr. Jayash Sharma, Mr. Ma-

hendra Kumar Pandey, Mr. Abhay Chaturvedi, Mr. Rajesh Kumar

Bathija, Mr. Pradeep Singh Bhati, Mr. Narbada Prasad Gupta, Mr.

Puneet Kumar Choudhary, Mr. Vikas Rai, Mr. Vijay Kumar Dixit,

Mr. Deepak Bhatia and Mr. Neeraj Jain for their scholastic guidance,

suggestions and encouragement throughout the study duration. Along

with them, I am also thankful to Mrs. Rita Saini and other Ph.D.

scholars for providing their kind helps and supports during the time of

research.

I owe special thanks to all my friends who encouraged and boosted

my morale during the difficult days particularly Mr. S C Gupta, Mr.

Lokendra Sharma, Mr. Shyam Sunder Agrawal, Mr. Sanjay Mishra,

Mr. Manish Agrawal, Mr. Hitendra Garg, Mr. Raj Kumar Verma,

Mr. Manish Oberoi, Mr. Amit Jaiswal, Mr. Anurag Saxena, Mr.

Suneet Parashar, Mr. Darpan Anand, Mr. Rahul Saraswat, Mr. B

N Gupta, Mrs. Nisha Agrawal, Mrs. Hemlata Yadav, Ms. Priyanka

Yadav, Mrs. Priyanka Agrawal, Mr. Gaurav Sharma, Mr. R N Singh,

Mr. P S Parihar, Mr. Santosh Sahu, Dr. A K Verma, Mr. Ajitesh

Kumar, Mr. Brajesh Sharma, Mr. Rahul Sighal, Mr. Nitin Sharma,

and Mrs. Neema Verma.

I would like to offer my most humble gratitude to my Late grandfather,

grand mother, and maternal grandmother.

I would like to offer my most humble gratitude to my maternal grand-

father Shri Ramsewak Neekhara, parents, my uncle and aunty, my

Mausaji and Mausiji, my Mamaji and Mamiji, my brother in-laws

Mr. Vishal Gupta and Mr. Saurabh Gupta, my brothers Neeraj,

Anand, Vinay, Naveen, Ram, Shayam, and Vaibhav, my sisters Su-

ruchi, Sonam, Kritika, Himanshi, and Chritanshi, my nephew Vikhyat,

my in-laws Shri S. L. Gupta, Mrs. Neelam Gupta, Mr. Santosh Gupta,

Mr. Manoj Gupta, Mr. Sachin Gupta and my wife Ruchi, my daughter

Khyati, my son Anant, and other family members for their unsurpassed

love, care, endless patience and countless sacrifices which they made

ix

for me. Indeed, a plethora of words would not suffice to say what I

owe to them.

Last but not the least I am highly tankful to Lord Shri Goverdhan Ma-

haraj Ji and Lord Shri Banke Bihari Lal Ji for showing his blessings

and brings this bright day in my career.

(Manish Gupta)

x

Abstract

Digital image watermarking is a process of imperceptibly embedding

watermark in the form of signature, random sequence or some image

into an image (host) which may be used to verify the genuineness of its

owner. Watermarking could be used for various real-time applications

like, protection of intellectual property rights (IPR) of multimedia con-

tents, forensics and piracy deterrence, content filtering, document and

image security, broadcast monitoring, etc. Robustness of digital image

watermarking methods are of paramount importance in the perspec-

tive of the protection of multimedia contents due to easily accessible

data manipulation tools and the strengthening of high data rate trans-

fer over the internet. Therefore, there is a growing interest in devel-

oping a method to protect multimedia contents. However, no single

watermarking method can be used to all applications. Therefore, the

primary objective of this work is to design and develop the robust dig-

ital watermarking methods for the protection of color images from its

unauthorise utilization.

The proposed watermarking methods consist of five steps. First, host

and watermark images are pre-processed using uncorrelated color space.

Second, the watermark is embedded into the host image using four dif-

ferent embedding methods. Third, post-processing is done by embed-

ding the coefficients into the watermarked image. Fourth, performance

of proposed watermarking methods is improved using various optimiza-

tion methods namely; genetic algorithm (GA), artificial bee colony

(ABC), and differential evolution (DE). Finally, the extracted water-

mark is verified for the genuineness of its owner. The performance of

developed methods are measured under various signal processing and

geometric attacks.

Generally, researchers used RGB, Y CbCr, YIQ, HSI, or HSV, etc. color

space models for both watermarks and host images in their digital

watermarking methods which are correlated in nature and impose the

restriction to use any one color component at a time for embedding

the watermark. However, there exists some uncorrelated color models

xii

such as Lab, Lαβ, etc., which may be used in image watermarking

to increase the robustness and quality by using all the color image

components of host and watermark images. Therefore, in this thesis,

a recently developed uncorrelated color space (UCS) has been utilized

during the pre-processing step. Experimental results show that the

UCS method outperforms other existing methods.

Embedding the watermark image into host image is the second step

of proposed watermarking method. The selection of embedding do-

main is the most vital and difficult task of any watermarking methods.

In this thesis, two transform methods have been used for embedding

the watermark into the host image namely; discrete wavelet transform

(DWT) and steerable pyramid transform (SPT). Both the methods

transform the host image and utilize their transform coefficients for

hiding the pre-process watermark data. After hiding the watermark

data into transform coefficients, post-processing is done to generate

watermarked image. The experimental results show that steerable

pyramid transform (SPT)-based method is better than other existing

methods of digital watermarking.

There is one component during the embedding process namely strength

factor which effects the performance of watermarking methods. There-

fore, there is a requirement to use its optimum value. For the same, the

proposed watermarking methods exploit optimization methods (GA,

ABC, and DE) to calculate the optimum strength factors. Among

other optimization methods, DE improves the performance as com-

pared to other existing methods.

Finally, the performance of the proposed methods are tested after ap-

plying various signal processing and geometric attacks. In this thesis,

20 different attacks have been used for validation of the proposed meth-

ods.

The output of this research work is a robust digital image watermarking

method for the protection of color images against the various malicious

attacks.

Keywords: Image watermarking, Discrete wavelet transform, Steer-

able pyramid transform, Uncorrelated color model, Genetic algorithm,

Artificial bee colony, Differential evolution.

xiii

Table of Contents

Title Page No.

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi

List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvi

List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .xviii

List of Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xx

Chapter 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.1 Motivation for Research . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Applications of Watermarking . . . . . . . . . . . . . . . . . . . . . 2

1.3 Requirements and Design Issues of Digital Watermarking Method . 4

1.4 Taxonomy of Watermarking Attacks . . . . . . . . . . . . . . . . . 7

1.5 Classification of Watermarking Methods . . . . . . . . . . . . . . . 10

1.6 Literature Review of Image Watermarking . . . . . . . . . . . . . . 12

1.6.1 Spatial Domain-based Methods . . . . . . . . . . . . . . . . 13

1.6.2 Transform Domain-based Methods . . . . . . . . . . . . . . 14

1.6.3 Optimization Methods in Image Watermarking . . . . . . . 20

1.6.4 Color Spaces in Image Watermarking Method . . . . . . . . 22

1.7 Challenges in Image Watermarking . . . . . . . . . . . . . . . . . . 24

1.7.1 Use of Color Watermark . . . . . . . . . . . . . . . . . . . . 24

1.7.2 Color Spaces . . . . . . . . . . . . . . . . . . . . . . . . . . 24

1.7.3 Transform Method . . . . . . . . . . . . . . . . . . . . . . . 25

1.7.4 Optimization Method . . . . . . . . . . . . . . . . . . . . . . 25

1.7.5 3-D Watermarking . . . . . . . . . . . . . . . . . . . . . . . 25

1.8 Scope of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Chapter 2 Digital Image Watermarking using Discrete Wavelet

Transform on Gray-Scale Watermark Image . . . . . . . 29

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

2.2 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

xiv

2.2.1 Discrete Wavelet Transform (DWT) . . . . . . . . . . . . . . 31

2.2.2 Uncorrelated Color Space (UCS) . . . . . . . . . . . . . . . 33

2.2.3 Genetic Algorithm (GA) . . . . . . . . . . . . . . . . . . . . 33

2.3 Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

2.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 39

2.5 Results and Discussions . . . . . . . . . . . . . . . . . . . . . . . . 44

Chapter 3 Digital Image Watermarking using Steerable Pyramid

Transform on Gray-Scale Watermark Images . . . . . . 45

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

3.2 Steerable Pyramid Transform (SPT) . . . . . . . . . . . . . . . . . 46

3.3 Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46



Chapter 4 Digital Image Watermarking using Discrete Wavelet

Transform on Color Watermark Images . . . . . . . . . . 57

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

4.2 Artificial Bee Colony (ABC) Method . . . . . . . . . . . . . . . . . 59

4.3 Differential Evolution (DE) Algorithm . . . . . . . . . . . . . . . . 61

4.4 Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

4.5 Method Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . 68



Chapter 5 Digital Image Watermarking using Steerable Pyramid

Transform on Color Watermark Images . . . . . . . . . . 81

5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

5.2 Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

5.3 Method Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . 85



Chapter 6 Conclusions and Scope for Future Work . . . . . . . . . 99

6.1 Contributions Made in the Thesis . . . . . . . . . . . . . . . . . . . 100

6.2 Scope for Future Work . . . . . . . . . . . . . . . . . . . . . . . . . 101

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .103

List of Publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .115

xv

List of Figures

Figure No. Title Page No.

1.1 Mutual dependency between the design parameters. . . . . . . . . . 5

1.2 Three main conflicting issues of watermarking. . . . . . . . . . . . . 5

1.3 Classification of watermarking methods. . . . . . . . . . . . . . . . 10

1.4 General block diagram of transform domain-based image water-

marking system. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.1 Three level decomposition layout of an image. . . . . . . . . . . . . 32

2.2 Genetic Algorithm Flow Chart. . . . . . . . . . . . . . . . . . . . . 34

2.3 Structural design of proposed method. . . . . . . . . . . . . . . . . 36

2.4 (a)-(b). RGB host images and (c)-(d). Gray scale watermark images. 39

2.5 Extracted RTU logo watermarks by proposed method after applying

considered attacks. . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

2.6 Extracted Aeroplane image watermarks by proposed method after

applying considered attacks. . . . . . . . . . . . . . . . . . . . . . . 43

3.1 Block diagram for steerable pyramid decomposition of an image. . . 47


3.3 (a)-(b). RGB host images and (c)-(d). Gray scale watermark images. 50

3.4 Extracted RTU logo watermarks by proposed method after applying

considered attacks. . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

3.5 Extracted Aeroplane image watermarks by proposed method after

applying considered attacks. . . . . . . . . . . . . . . . . . . . . . . 54


4.2 Watermark division. . . . . . . . . . . . . . . . . . . . . . . . . . . 65

4.3 Partitioning of 3-DWT coefficients. . . . . . . . . . . . . . . . . . . 66

4.4 RGB host images. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

4.5 RGB watermark images. . . . . . . . . . . . . . . . . . . . . . . . . 70

4.7 Three level decomposed host lena image using DWT. . . . . . . . . 70

4.6 UCS host and watermark images (a). Lena, (b). Mandrill, (c).

Pepper, (d). Sailboat, (e). RTU Logo, and (f). Aeroplane. . . . . . 71

xvi

4.8 Representative scrambled UCS watermark images. . . . . . . . . . . 71

4.9 RGB watermarked images embedded by using DE-based proposed

method (a)-(d) RTU logo and (e)-(h) Aeroplane image . . . . . . . 72

4.10 Comparison of extracted watermarks by considered and proposed

methods along with their corresponding NC values. Columns shows

extracted watermarks from watermarked image namely (a). Lena,

(b). Mandrill, (c). Pepper, and (d). Sailboat using the considered

and proposed methods mentioned in first column. First five rows

shows the extraction of RTU logo while last five shows extraction

of aeroplane watermark image. . . . . . . . . . . . . . . . . . . . . 74

4.11 Extracted RTU logo watermarks by proposed method using DE

after applying considered attacks. . . . . . . . . . . . . . . . . . . . 78

4.12 Extracted Aeroplane watermarks by proposed method using DE

after applying considered attacks. . . . . . . . . . . . . . . . . . . . 79


5.2 Watermark division. . . . . . . . . . . . . . . . . . . . . . . . . . . 84

5.3 RGB host images. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

5.4 RGB watermark images. . . . . . . . . . . . . . . . . . . . . . . . . 87

5.5 UCS host and watermark images (a). Lena, (b). Mandrill, (c).

Pepper, (d). Sailboat, (e). RTU Logo, and (f). Aeroplane. . . . . . 88

5.6 Representative scrambled UCS watermark images. . . . . . . . . . . 88

5.7 RGB watermarked images embedded by (a)-(d) RTU logo and (e)-

(h) Aeroplane image . . . . . . . . . . . . . . . . . . . . . . . . . . 89

5.8 A comparison of before and after optimization of fitness values for

30 runs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

5.9 Comparison of extracted watermarks by considered and proposed

methods along with their corresponding NC values. Columns shows

extracted watermarks from watermarked image namely (a). Lena,

(b). Mandrill, (c). Pepper, and (d). Sailboat using the considered

and proposed methods mentioned in first column. First four rows

shows the extraction of RTU logo while last four shows extraction

of aeroplane watermark image. . . . . . . . . . . . . . . . . . . . . 91

5.10 Extracted RTU logo watermarks by proposed method using UCS

and DE after applying considered attacks. . . . . . . . . . . . . . . 95

5.11 Extracted Aeroplane watermarks by proposed method using UCS

and DE after applying considered attacks. . . . . . . . . . . . . . . 96

xvii

List of Tables

Table No. Title Page No.

1.1 Applications of watermarking methods. . . . . . . . . . . . . . . . . 3

1.2 Requirements and design issues of watermarking methods. . . . . . 5

1.3 Taxonomy of watermarking attacks. . . . . . . . . . . . . . . . . . . 8

1.4 Categories of spatial domain-based image watermarking method. . . 14

1.5 Categories of transform domain-based image watermarking method. 17

1.6 Categories of optimization methods in image watermarking. . . . . 21

1.7 Categories of color spaces in image watermarking method. . . . . . 23

2.1 Comparison of CPSNR values of watermarked images resultant

from proposed and considered method. . . . . . . . . . . . . . . . . 40

2.2 Comparison of robustness in terms of NC values obtained after ap-

plying attacks on the watermarked images. . . . . . . . . . . . . . . 41

3.1 Comparison of CPSNR values of watermarked images resultant

from proposed and considered methods. . . . . . . . . . . . . . . . . 51


plying attacks on the watermarked images. . . . . . . . . . . . . . . 52

4.1 Setting of the parameters of three optimization methods namely;

GA, ABC, and DE. . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

4.2 Comparison of CPSNR and SSIM values of watermarked images

resultant from proposed and considered methods. . . . . . . . . . . 72

4.3 Average subjective quality comparison of original and watermarked

images by 10 human beings in the scale of 0 to 5. . . . . . . . . . . 72


plying common signal processing attacks on the watermarked image

embedded with RTU logo . . . . . . . . . . . . . . . . . . . . . . . 75



embedded with Aeroplane image. . . . . . . . . . . . . . . . . . . . 76

xviii


plying geometric attacks on the watermarked images. . . . . . . . . 77

5.1 Comparison of CPSNR and SSIM values of watermarked images

resultant from proposed and considered methods. . . . . . . . . . . 90

5.2 Average subjective quality comparison of original and watermarked

images by 10 human beings in the scale of 0 to 5. . . . . . . . . . . 90



embedded with RTU logo . . . . . . . . . . . . . . . . . . . . . . . 92



embedded with Aeroplane image. . . . . . . . . . . . . . . . . . . . 93


plying geometric attacks on the watermarked images. . . . . . . . . 94

xix

List of Abbreviations

ABC Artificial Bee Colony

ACO Ant Colony Optimization

CPSNR Composite Peak Signal-to-Noise Ratio

DCT Discrete Cosine Transform

DE Differential Evolution

DFT Discrete Fourier Transform

DWT Discrete Wavelet Transform

EA Evolutionary Algorithm

GA Genetic Algorithms

HSI Hue Saturation Intensity

HSV Hue Saturation Value

HVS Human Visual System

ICA Independent Component Analysis

IDCT Inverse Discrete Cosine Transform

IDFT Inverse Discrete Fourier Transform

IDWT Inverse Discrete Wavelet Transform

IPR Intellectual Property Rights

ISPT Inverse Steerable Pyramid Transform

JPEG Joint Photographic Expert Group

LSB Least Significant Bit

MPEG Moving Picture Expert Group

NC Normalized Correlation

PCA Principal Component Analysis

PSO Particle Swarm Optimization

QS Quantization Step

RGB Red Green Blue

SA Simulated Annealing

SPIHT Set Partitioning In Hierarchical Trees

SPT Steerable Pyramid Transform

SSIM Structural Similarity

xx

SV Singular Value

SVD Singular Value Decomposition

UCS Uncorrelated Color Space

Y CbCr Luminance Chroma: Blue Chroma: Red

YIQ Luminance In-phase Quadrature

YUV Luminance Blueluminance Redluminance

xxi

Chapter 1

Introduction

The digital image watermarking method is capable to resolve the issue of ownership

and hence, this area of research has a wider set of applications such as copyright

protection, content authentication, ownership identification, etc. Bender et al. [1],

Fotopoulos et al. [2], Potdar et al. [3], and Liu et al. [4] presented a state-of-the-

art survey of image watermarking methods which have been applied in various

applications. This thesis is an attempt to develop a digital image watermarking

method for copyright protection. This chapter presents the motivation of the

research, application areas, requirement and design issues, taxonomy of attacks,

and classification of watermarking methods. Further, some of the challenges in the

field of image watermarking are identified. Moreover, a detailed survey of relevant

research in digital image watermarking is presented.

1.1 Motivation for Research

In current digital era, the orderly growth of easily capturable multimedia data

devices (such as digital cameras, camcorders, and scanners), efficient compres-

sion algorithms for multimedia data, and digital data transmission speed over the

internet have enable widespread applications, which rely on digital data. Digital

multimedia data offers many advantages over its analog counterpart like high qual-

ity, easy editing, easily stored and copying without loss of fidelity, etc. Though,

the digital data possesses many inherent advantages over its analog version, the

genuineness or ownership is the biggest challenge. The digital multimedia data can

be easily duplicated and/ or manipulated which results the real threat for content

owner from the misuse of their work or data. To keep on with the transmission

of multimedia data over the high speed internet the reliability and originality of

the transmitted multimedia data should be verifiable. In view of these deleterious

findings, it is necessary and need of current digital era that the multimedia data

must be protected and secured against the illegal utilization.

Today, multimedia content owners are eagerly seeking technologies that promise

to protect their rights and secure their content from piracy, unauthorized usage,

and enable the tracking and conviction of media pirates. During the past decades,

various solutions were proposed by the researchers in order to protect the multime-

dia data against the unauthorized utilization. One of the solutions of this problem

is to embed an invisible data into the original multimedia data to proof the own-

ership of the data. This type of method is named as information hiding which can

be further classified into various sub-classes such as cryptography, steganography,

and watermarking [5, 6]. Cryptography is the most common method of protecting

digital multimedia data, where the multimedia content is encrypted preceding to

release and a decryption key is provided to those who have purchased genuine or

legal copies [7, 8, 9]. However, cryptography cannot assist the content providers

monitor their contents after the decryption process; a buccaneer could easily pur-

chase a genuine or legal copy and then resell it or distribute it for free over a

public network. Further, steganography is about preventing the detection of an

encrypted data, which has been protected by cryptography algorithms. However,

the message hide by steganography is not robust. Watermarking has an extra

requirement of robustness compared to steganography algorithms against various

signal processing and geometric attacks. It is therefore vital to find a path to pro-

tect these digital multimedia contents with a more precise method, which would

enable the content owners to get confidence in placing and distributing their mate-

rial over the internet. Watermarking could be such a vehicle. Therefore, this thesis

plans to design and develop an efficient and robust digital image watermarking

method for protecting the color images against various attacks.

Following section illustrates the various applications of watermarking methods.

1.2 Applications of Watermarking

Digital watermarking methods have many applications namely; copyright pro-

tection, content authentication, fingerprinting, broadcasting monitoring, secret

communication, medical safety, etc. [10, 11, 12, 13] as discussed in the following

sections and summarized in Table 1.1.

• Copyright Protection: One of the motivations of developing the water-

marking methods is copyright protection. In this application, a copyright

data/ information is embedded into host object without loss of quality [14].

The embedded data prevents other parties from claiming the ownership of

2

Table 1.1: Applications of watermarking methods.

S.No. Application Description

1. Copyright Protection The watermark must be known only to the author and also mostimportantly it must be robust against the various attacks.

2. Covert Communication Since various offices or governments put a ceiling on the useof encryption. In this scenario people may send their secretmessages by using the watermarking method.

3. Copy Control This application restrict the illegally copying of copyrighted ma-terials by embedding a never-copy watermark or limiting thenumber of times of copying.

4. Content Authentication Fragile watermark could be embedded into the host image tocheck the authenticity of the data.

5. Fingerprinting Fingerprinting method used by the owner is to trace the sourceof illegal copies.

6. Broadcast Monitoring Owners of copyrighted programs needs to know about illegalbroadcast, aired by the broadcasters, at the time and locationthat they want according to the contracts terms and conditions.

7. Medical Safety Embedding the date and the patients name in medical imagescould be a useful safety measure.

8. Indexing Indexing of multimedia contents like movies, news items, videomail, images, etc., to help search engines to search those contentsover the internet.

that data. Moreover, the watermark must be known only to the author and

must be robust against the various attacks.

• Covert Communication: Watermarking methods can also be used for

the covert information transmission, as various offices or governments put

a ceiling on the use of encryption. In this scenario people may send their

secret messages by using the watermarking methods.

• Copy Control: This application restricts the illegal copying of copyrighted

materials by embedding a never-copy watermark or limiting the number of

times of copying. For example, today many documents are available on the

internet which could not be saved and printed to control the illegal copying.

• Content Authentication: Fragile watermark could be embedded into the

host image to check the authenticity of the data. A fragile watermark indi-

cates whether the data has been altered and also delivers the information as

to where the data was altered. Therefore, this application does not demand

the robust watermark, since we have to detect the changes only.

• Fingerprinting: Fingerprinting method, used by the owner, is to trace the

source of illegal copies. To achieve this, owner can embed different water-

marks into each copy that distributed to a different customer. For example,

3

unique serial numbers are assigned to customers and used to identify the

customer.

• Broadcast Monitoring: Owners of copyrighted programs needs to know

about illegal broadcast or the commercials, aired by the broadcasters, at

the time and location that they want according to the contracts terms and

conditions. Watermarks can be embed in any type of data to broadcast on

the network by automated systems, which are able to monitor distribution

channels to track the content in the time and the place that they appear.

• Medical Safety: Recently, telemedicine facilitates medical diagnosis by

sending patient medical data/ report over the public network for further

analysis where the modern medical equipments are available. These equip-

ments produce large amount of data every day. Hence, it is necessary to

protect these crucial data. Medical image watermarking is a suitable method

used for enhancing security and authentication of medical data, which is used

for further diagnosis and reference. Embedding the date and the patients

name in medical images could be a useful safety measure.

• Indexing: One of the well-known application of the watermarking is index-

ing of multimedia contents like movies, news items, video mail, images, etc.

In which a comments or any tag/ level is embedded on the contents, so that

these comments or tags are utilized by any search engine to search those

contents over the internet.

The following section listed various requirements and design parameters related

to watermarking methods.

1.3 Requirements and Design Issues of Digital

Watermarking Method

There are various design issues and requirements associated with any watermark-

ing method like transparency, robustness, capacity, security, etc. as summarized

in Table 1.2. The objectives of researchers in the field of watermarking is to maxi-

mize all these parameters for a particular method. Furthermore, these parameters

are mutually dependant on each other as shown in Figure 1.1. Three parameters

namely; transparency, robustness, and capacity are inversely related to each other

i.e. the transparency of a watermarking method increases then its robustness

suffers and vice-versa. This relationship has been depicted in Figure 1.2.

4

Table 1.2: Requirements and design issues of watermarking methods.

S.No. Requirements and DesignIssues

Description

1. Transparency No visual or audio effect should be noticed by the user.

2. Robustness Watermark can be robust against one operation on host dataand may be fragile against another operation.

3. Capacity If the capacity is higher than the better robustness is achievedand at the same time transparency suffers or vice-versa.

4. Security The watermark must resist against the attacks on the hostdata.

5. Complexity The computational cost must be as low as possible to makethe applications real time.

6. Reliability Watermark data embedded into the host, must be recoverablewith the acceptable errors.

Figure 1.1: Mutual dependency between the design parameters.

Figure 1.2: Three main conflicting issues of watermarking.

Hence, the relative importance of these parameters depends on the application-

to-application as listed in the previous section. Moreover, certain applications

demand for more robustness compared to the transparency of the method viz.

copyright protection. Therefore, watermarking method design process involves

trade-off between the conflicting requirements parameters. The most important

requirement for digital watermarking are summarized below:

• Transparency : Transparency or imperceptibility refers to the correlation/

similarity between the watermarked data and the original data. The wa-

5

termark should be invisible. In other words, there is no visual or audio

effect should be noticed by the user. The watermark should not be disgrace

the quality of the host data. However, for a particular application minute

degradation in the host data is permissible to achieve better robustness or

to optimize the cost.

• Robustness: If a watermark can stay alive after common signal processing

operations (such as compression, filtering, translation, rotation operations,

analog-to-digital conversion, scaling, etc.) on host data, then such type of

watermark is called the robust. Moreover, watermark can be robust against

one operation on host data and may be fragile against another operation. For

certain applications, there is a need to embed a robust watermark into the

host data, while some applications demand the fragile watermark. Hence,

it also depends on the application. If the watermark data is embedded in

significant area of a host image then the better robustness is achieved. This

is because those area do not alter so much after common image processing

operations [15]. Contrary to robust watermark, a fragile watermark is not

designed to be robust.

• Capacity : A capacity or payload refers the amount of watermark data that

can be embedded into host. For example, the capacity in case of image

watermarking means the number of bits embedded within the host image.

If the payload is higher than the better robustness is achieved and at the

same time transparency suffers or vice-versa. Therefore, the payload of the

embedded watermark must be in sufficient amount to enable the envisioned

application.

• Security : The watermark must resist against the attacks on the host data.

It must be impossible for an attacker to delete or modify the watermark

without rendering the multimedia data unusable. From this point of view, a

secret watermark key is also used in watermarking, so that it is not possible

to retrieve or even modify the watermark without knowledge of the key.

• Complexity : Depending on the application, the watermark detection is to

be done at different speeds and complexity. For example in broadcast mon-

itoring application, the detection of the watermarking is done in real time.

The computational cost must be as low as possible to make the applications

real time. To keep above facts, the complexity of the watermarking methods

should be low.

6

• Reliability : Watermark data, embedded into the host, must be recoverable

with the acceptable errors.

The performance of watermarking methods for achieving above mentioned re-

quirements are tested after applying various attacks. Therefore, the following

section describes the classification of attacks applicable to image watermarking.

1.4 Taxonomy of Watermarking Attacks

Any procedure that can decrease the performance of watermarking method may be

termed as attack. Testing the robustness and security of a watermarking method

against attacks is as important as the design process. The attacks do not always

remove or destroy the watermark but, also disable its detection. The distortions

done by any attacks degrade the performance of the watermarking method.

In general, different attacks on watermarking can be divided into two classes

namely; unintentional and intentional attacks. To achieve the high reliability

of watermark detection, the watermark detection process has to be robust to the

modifications in the host data caused from both unintentional and intentional

attacks. Unintentional attacks take place using signal processing operations on

watermarked data namely; compression, printing, scanning, filtering, noise, geo-

metric transforms, cropping, etc. For example, multimedia data is generally stored

in lossy compressed format in order to use less storage capacities. These compres-

sion algorithms discard the unimportant parts of data. This distortion may cause

damage of inserted watermark data too. This means that a simple attack is com-

pressing multimedia data in a lossy way. In addition, a rotation or scaling can

change pixel values and destroy the watermark data. Signal processing operations

such as quantization, decompression, re-sampling, and color reduction can dam-

age the watermark. For intentional attacks, a person on purpose can attack on

inserted watermark data in order to copy the multimedia data. In both cases,

any watermarking method should be able to detect and extract the watermark

after attacks. The taxonomy of various intentional and unintentional attacks in

watermarking methods [16, 17, 18], are presented in Table 1.3 and their details

are summarized below:

• Noise: Any random unwanted signal with a given distribution namely; Gaus-

sian, salt & pepper, Poisson, etc., is added to the image unintentionally.

This type of noise may be added during the Analog-to-Digital conversion

and vice-versa, or as a result of transmission errors. However, an attacker

may introduce perceptually shaped noise with the maximum un-noticeable

7

Table 1.3: Taxonomy of watermarking attacks.

S.No. Attack Details

1. Noise Any random unwanted signal with a given distribution namely; Gaus-sian, salt & pepper, Poisson, etc., is added to the image unintention-ally.

2. Filtering Filtering attacks are linear filtering namely; low pass/ mean filtering,Gaussian, and sharpening filtering, etc.

3. Compression If the watermark is required to resist different levels of compression, itis usually advisable to perform the watermark embedding in the samedomain where the compression takes places.

4. Multiple Watermarking The one of the solution of such type of problem is to embedding thetime information by a certification authority.

5. Geometrical Attacks Geometrical attacks distort the watermark through spatial alterationsof the watermarked image. Common geometrical attacks are rotation,scaling, etc.

6. Cropping This is a very common attack which crops the region of interest fromthe watermarked object.

7. Watermark Removaland Interference At-tacks

The objective of such attacks is to forecast or estimate the watermark.

8. Statistical Averaging The objective of such attacks is to recover the host image and/orwatermark data by statistical investigation of multiple marked datasets.

power. This will characteristically force to increase the threshold at which

the correlation detector operates.

• Filtering : Filtering attacks are linear filtering namely; low pass/ mean filter-

ing, Gaussian, and sharpening filtering, etc. Mean or average filtering does

not introduce considerable degradation in watermarked images but, can dra-

matically affect the performance. Therefore, to design a watermark, robust

to a known group of filters that might be applied to the watermarked image,

the watermark data should be designed in such a way that it have most of

its energy in the frequencies which filter transfer functions changes the least.

• Compression: Compression belongs to an unintentional attack class, which

appears very often in various multimedia applications. In practice, most of

the images, audio, and video are being transmitted/ distributed via internet

after the compression in order to reduce the time and data usage. If the

watermark is required to resist different levels of compression, it is usually

advisable to perform the watermark embedding in the same domain where

the compression takes places.

• Multiple Watermarking : An attacker may watermark an already water-

marked data and later claims of ownership. One solution to such type of

8

problem is to embed the time information by a certification authority.

• Geometrical Attacks : Geometrical attacks do not pretend to remove the wa-

termark by itself, but to distort it through spatial alterations of the water-

marked image. With such attacks watermarking detector loses the synchro-

nization with the embedded information. These attacks can be subdivided

into attacks applying general affine transformations and attacks based on

projective transformation. Common geometrical attacks are rotation, scal-

ing, change of aspect ratio, translation and shearing, etc.

• Cropping : This is a very common attack since in many cases the attacker is

interested in a small portion of the watermarked object, such as parts of a

certain picture or frames of video sequence. With this in mind, in order to

survive, the watermark needs to be spread over the dimensions where this

attack takes place.

• Watermark Removal and Interference Attacks: The objective of such attacks

is to forecast or estimate the watermark and then use the predicted water-

mark either to eradicate watermark or to damage its unique extraction at the

destination side. Some known efficient removal attacks are; the median wa-

termark prediction followed by subtraction [19], the Wiener prediction and

subtraction [18] and perceptual re-modulation [20], which combines both

removal and interference attacks.

• Statistical Averaging : The objective of such attacks is to recover the host

image and/or watermark data by statistical investigation of multiple marked

data sets. An attacker may attempt to predict the watermark and then to

remove the watermark by subtracting the estimate. This is very hazardous

if the watermark does not rely significantly on data. That is why the percep-

tual masks are used to create a watermark. Averaging or smoothing attack

is belonging to this class of attack. Averaging attack consists of averaging

many instances of a given data set (e.g. N) each time marked with a different

watermark. In this pattern a prediction of host data is calculated and each

of the watermarks is weakened by a factor N.

In the above section, various classification of watermarking attacks have been

presented. The following section gives the brief details of classification of the

different watermarking methods.

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1.5 Classification of Watermarking Methods

Mohanty [21] presented a state-of-the-art categorization of digital watermarking

methods. Digital watermarking methods may be categorized on the basis of host

multimedia data, human perception, embedding domain, robustness, data extrac-

tion, application area, etc. [13]. Figure 1.3 shows the classifications of digital

watermarking methods. There are different way of classification of digital water-

Figure 1.3: Classification of watermarking methods.

mark methods [22].

• First, watermarking methods may be divided into four groups according to

the type of host multimedia data to be watermarked;

– Image watermarking method

– Audio watermarking method

– Video watermarking method

– 3-dimension (3-D) watermarking method

• Second, watermarking methods may be grouped on the basis of the data for

extraction;

– Private or non-blind watermarking method : This class of method re-

quired the original data (watermark or/ and host) during the extraction

of watermark from the watermarked data.

10

– Semi-private or semi-blind watermarking method : This group of method

needs extra information other than the original data during the detec-

tion.

– Public or blind watermarking method : In public or blind watermarking

method the detection of watermark from the watermarked data needs

only the watermark.

• Third, watermarking methods may be categorized on the basis of human

perception;

– Visible watermarking method : If the watermark data is noticeable to

the user, then such class of watermarking is known as visible water-

marking method. Examples of visible watermarks are logos that are

used in papers and video.

– In-visible watermarking method : If the watermark data is imperceptible

to the user, then such class of watermarking is known as invisible wa-

termarking method. For example, images distribute over the internet

and watermarked invisible for copy protection.

• Fourth, watermarking methods may also be classified on the basis of robust-

ness;

– Fragile: A fragile watermark will be changed if the host data is modi-

fied.

– Semi-fragile: Semi-fragile watermark is sensitive to some degree of the

change to a watermarked image.

– Robust : Watermark in robust method cannot be removed by common

signal processing operations.

• Fifth, watermarking methods may also be classified on the basis of embed-

ding watermark data;

– Text : If the embed watermark data is in the nature of text.

– Image format : If the embedded watermark belongs to any image shape

like logo, binary image, gray scale image, color image or logo, stamp,

etc.

– Noise sequence: In this class of method the embedding watermark is

in terms of random noise sequence like pseudo random or Gaussian

random sequence etc.

11

• Sixth, watermarking methods may be categorized on the basis of application

of method;

– Source-based watermarking method : In source based, all copies of a

particular data have a unique watermark, which identifies the owner of

that data.

– Destination-based watermarking method : In this method, each dis-

tributed copy is embedded using a unique watermark data, which iden-

tifies a particular destination.

• Finally, watermarking methods may be classified into two major classes ac-

cording to the embedding domain;

– Spatial domain

– Transform domain;

∗ Discrete Fourier Transform (DFT) domain-based watermarking method

∗ Discrete Cosine Transform (DCT) domain-based watermarking method

∗ Discrete Wavelet Transform (DWT) domain-based watermarking

method

∗ Steerable Pyramid Transform (SPT) domain-based watermarking

method

∗ etc.

Following section reviews various image watermarking methods along with their

constraints.

1.6 Literature Review of Image Watermarking

In the previous Sections 1.2 – 1.5 the various application areas, requirements and

key design issues, classification and need of different watermarking attacks, and

classification related to digital watermarking methods have been discussed. In

the area of watermarking, image watermarking particularly attracted the research

community a lot because of following reasons;

• Easily availability of the test image databases.

• It contains adequate superfluous information to give an opportunity for em-

bedding the watermark data easily.

• Any successful image watermarking method may be upgraded for the video

also.

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• Wider set of applications.

Therefore, most of the research work in the field of watermarking is dedicated to

image as compared to audio, video, and other multimedia formats [23]. Therefore,

in this thesis the emphasis has been given only on the literature review of image

watermarking methods.

Digital image may be represented/ stored either in spatial/ time domain or

in transform domain. The spatial/ time domain image is characterize by pix-

els, whereas the transform domain image is described in terms of its transform

coefficients. In other words, transform domain representation of an image seg-

regates the transform coefficients into multiple frequency bands. To convert an

image to its transform domain representation, we can use various available re-

versible transform methods namely; Discrete Fourier Transform (DFT), Discrete

Cosine Transform (DCT), Discrete Wavelet Transform (DWT), Steerable Pyra-

mid Transform (SPT), etc. Each of these transform method has its own specific

characteristics and representation of an image.

Digital image watermarking is a process of imperceptibly hiding a watermark

(in the form of signature, random sequence, or some image) into an image (host

or cover) which may be used to verify the genuineness of its owner. The resultant

image of this process is termed as watermarked image. The watermarking meth-

ods can be performed either in spatial domain or in the transform domain. In

spatial domain-based watermarking method, watermark may be embedded within

an image by modifying the pixel values [9] or the Least Significant Bit (LSB) val-

ues. While, in transform domain-based watermarking method, watermark may be

embedded by modifying the transform domain coefficients. However, more robust

watermark could be embedded in the transform domain of images by modifying

the transform domain coefficients as compared with the spatial domain-based im-

age watermarking method. In the following section, the state-of-the-art review of

digital image watermarking methods have been presented based on spatial domain

and transform domain [1, 2, 3, 22, 24, 25, 26].

1.6.1 Spatial Domain-based Methods

A watermarking method based on the spatial domain approach, hides watermark

data in the pixel values of the host image. Such class of methods make minor

changes in the intensity of pixel value of host image [1, 27, 28, 29, 30, 31, 32, 33, 34].

One of the most common examples of this method is to embed the watermark in

the LSB’s of image pixels [28, 30, 35]. In other words, significant portions of

low frequency components of images should be modified in order to insert the

13

watermark data in a reliable and robust way. As another example, an image is

divided into the same size of blocks and a certain watermark data is added with

the sub-blocks [28]. The imperceptibility of the watermark data is achieved on the

postulation that the LSB bits are visually insignificant. Although, spatial domain-

based watermarking method can be easily implemented and very fast, they have

the many disadvantages. These methods are highly susceptible to common signal

processing operations and can be easily impaired and tempered. For example,

lossy compression could completely crush the watermark data. In summary, wa-

termarking method based on spatial is very easy to destroy using some attacks like

low-pass filtering, additive noise, etc. In other words, the spatial domain-based

image watermarking methods are not robust against the common signal process-

ing operation on the host image. The brief summary of spatial domain-based

methods presented in Table 1.4. Researchers [15] suggested that the transform

domain-based image watermarking methods are more robust as compared to spa-

tial domain-based image watermarking methods against the various watermarking

attacks as mentioned in the Section 1.4. Therefore, further in this thesis our focus

is only on the transform domain-based image watermarking methods.

Table 1.4: Categories of spatial domain-based image watermarking method.

S.No. Category Property Description

1. Pixel-based[27, 28, 33]

Such class of methods makes mi-nor changes in the intensity ofpixel value of host image to em-beds a watermark. One of themost common example of thismethod is to embed the water-mark in the LSB’s of image pix-els.

Spatial domain-based water-marking method can be easilyimplemented and very fast.

2. Block-based[28, 29, 30]

In this class of watermarkingmethod an image is divided intothe same size of blocks and acertain watermark data is addedwith the sub-blocks.

These methods are highly sus-ceptible to common signal pro-cessing operations and canbe easily impaired and tem-pered. For example, compres-sion could completely crush thewatermark data.

1.6.2 Transform Domain-based Methods

The transform of an image is just another form of representation. It does not

change the content present in the image. Transform domain-based image wa-

termarking methods have many advantages over spatial domain-based methods

[15]. As presented in literature, transformed domain-based image watermarking

methods are more robust against the various watermarking attacks and signal

processing operations because the transform domain does not utilize the original

14

host image for casting the watermark data. In addition, the transform domain-

based image watermarking distributes the watermark data over all part of the

host image. Moreover, transform domain-based methods are capable enough to

embed more watermark bits into the host image and are more robust to attack.

However, they are difficult to implement and are computationally more expensive

as compared with the methods given in Section 1.6.1. In literature, various re-

versible transform methods namely; DFT, DCT, DWT, SPT, etc. are used by the

researcher to improve the robustness of the image watermarking methods. This

class of watermarking methods insert the watermark data into the host image by

manipulating the corresponding transform coefficients [36, 37, 38, 39, 40, 41, 42,

43, 44, 45, 46, 47, 48].

Figure 1.4 shows the general block diagram of transform domain-based digital wa-

termarking system. Summary of the transform domain-based methods are given

in Tables 1.5. A detailed sate-of-the-art survey of transform domain-based image

watermarking methods has been presented by Potdar et al. [3].

Discrete Fourier Transform (DFT)-based Method

Ruanaidh et al. [49] presented a DFT-based image watermarking method in which

the watermark data is inserted into the host image by manipulating the phase

information. Wolfgang et al. [30] later concluded in their work that image water-

marking using the phase manipulation is robust against image contrast operation.

Further, Ruanaidh and Pun [50] presented a method of image watermarking using

the Fourier transform and concluded that method is robust against the geometric

attacks. Lin at al. [51] presented a novel image watermarking method which is ro-

bust against the rotation, scaling, and translation attacks. However, this method

is not robust enough against the cropping and compression attacks. In litera-

ture, few methods are available where the watermark is casted by modifying the

mid frequency band of DFT magnitude component [52, 53]. They concluded that

the proposed methods are robust against the Joint Photographic Expert Group

(JPEG) and Set Partitioning In Hierarchical Trees (SPIHT) compression attacks.

Moreover, Solachidis and Pitas [54] presented a novel method of image watermark-

ing in which a circularly symmetric watermark is embedded in the DFT domain.

In addition, the proposed method is robust against the geometric rotation attacks

because the watermark is circular in shape with its center at image center.

15

(a) Embedder (b) Extraction

Figure 1.4: General block diagram of transform domain-based imagewatermarking system.

Discrete Cosine Transform (DCT)-based Method

Transform domain-based image watermarking methods possess a number of de-

sirable properties as compared to spatial domain-based methods. Moreover, these

methods make it difficult for any intruder or unauthorized user to read or change

the watermark data. Since in the transform domain, embedded watermark is dis-

tributed over the area of the image after the inverse transformation. The DCT

domain-based method is divided into two groups namely; global-based and block-

based DCT image watermarking methods. The image watermarking methods that

rely on the global DCT approach, spread the watermark over the entire image.

On the other hand, block-based approach embeds the watermark as follows:

1. Divide the host image into non-overlapping blocks of 8× 8.

2. Take the DCT to each of the block as mentioned in step 1.

3. Choose a specific block for watermark embedding by using certain criteria

like Human Visual System (HVS).

4. Choose coefficients for watermark embedding by using certain selection cri-

teria like highest or lowest magnitude.

5. Cast the watermark data by modifying the selected coefficients; and

16

Table 1.5: Categories of transform domain-based image watermarking method.

S.No. Category Description

1. DFT-based Robust against image contrast operation. [49]

Robust against the geometric attacks. [50]

Robust against the rotation, scaling, and translation attacks.However, this method is not robust enough against the crop-ping and compression attacks. [51]

Watermark embedded by modifying the mid frequency band ofDFT coefficients. Furthermore this method is robust againstJPEG and SPIHT compression attacks. [52], [53]

Circularly symmetric watermark is embed in the DFT domainand concluded that method is robust against the geometricrotation attacks. [54]

2. DCT-based Global DCT approach by exploiting the HVS and shows thatthe method is robust against the geometric attacks like rota-tion, scaling, etc. [8], [15]

Block-based DCT method. DCT-based methods missing thetime and frequency information at the same time. [55], [56]

3. DWT-based Based on principle of “toral automorphism”. [31]

Known as “cocktail watermarking”and concluded that themethod is robust against the all possible watermarking attacks.[57]

Presented a novel method for any size of images, which hideswatermark into the high-frequency sub-bands of DWT coeffi-cients. [58]

Uses Daubechies-2 filter bank for transformation of host imageand show that the proposed method is robust to geometric,filtering, and StirMark attacks. [59]

Uses Symlet-8 filter bank and shows that the method is robustagainst various attacks. [60]

Uses Har wavelet for host image transformation. [61]

Uses Symlet-4 filter bank. [42]

4. SPT-based Robust against the various geometric attacks in comparisonwith DWT-based method. [46]

Hybrid watermarking method using the SPT and SVD, thismethod have good visual quality and resistance against severalattacks. [62]

Robust against the common signal processing and geometricattacks like rotation. [63]

6. Take inverse DCT (IDCT) transform on each block.

In the DCT-based image watermarking method, most of the research is dedicated

to design the specific criteria for selecting the particular block and coefficients.

Cox et al. [15] proposed a robust image watermarking method using the global

DCT approach, which embeds the imperceptible watermark data into the host

image by exploiting the HVS. Koch et al. [8] reported a method for watermark

embedding having following steps:

1. Divide the host image into non-overlapping blocks of 8× 8.

2. Take the DCT to each of the block as mentioned in step 1.

17

3. Choose the specific block by using the pseudo-random subset criteria.

4. A triplet of frequencies is selected from 1 of 18 predetermined triplets.

5. Cast the watermark data by modifying the selected coefficients, so that their

relative strengths encode a 1 or 0 value.

6. Take inverse DCT transform on each block.

In literature, various image watermarking methods have been proposed by using

DCT [55, 56, 64, 65, 66, 67, 68, 69]. Out of the existing DCT-based watermarking

methods, the block-based DCT method is widely used by researchers in the area of

watermarking. Lin et al. [69] find that the DCT-based methods are robust against

JPEG compression, but as robustness increases the quality of watermarked image

decreases. Moreover, DCT-based methods are not robust against the geometric

attacks like rotation, scaling, etc.

Discrete Wavelet Transform (DWT)-based Method

To achieve the robustness of image watermarking methods, discrete wavelet trans-

form utilizes the spatial and frequency information of the transform data in multi-

ple resolution. Recently, many image watermarking methods have been reported

which exploit the advantage of DWT over the DFT and DCT [40, 41, 42, 58, 59,

60, 70, 71, 72, 73, 74]. However, the performance of DWT-based method can be

further enhanced by exploiting the characteristics of HVS during the watermark

embedding stage. If a watermarking method can utilize the characteristics of the

HVS, then it is possible to embed watermark with more energy in a host image,

which makes watermark more robust. Although, the HVS model enhances the

imperceptibility and robustness of watermarking method, it suffers with the com-

putational cost and complexity point of view. According to the HVS, the human

eye is less sensitive to noise in high resolution DWT bands having an orientation

of 450. From this point of view, the DWT is a very useful transform as compared

to DFT and DCT, since it can be used as a computationally efficient version of

the frequency model for the HVS [70]. One of the reasons for the popularity of

DWT-based image watermarking method is that various multimedia standards

like JPEG2000, MPEG-4, etc. are based on the DWT. Hence, DWT decompo-

sition can be exploited to make a real-time watermark application. DWT-based

image watermarking methods are capable to embed a fairly good quality of wa-

termark and it can recover the watermark from watermarked image effectively.

The quality and robustness of DWT-based methods depend on the selection of

18

particular filter bank and decomposition level [61]. Voyatzis and Pitas [31] devel-

oped a robust method on the principle of “toral automorphism”, in which they

embed the binary logo watermark. Lu et al. [57] presented a method in which

they embedded dual watermarks with complement to each other. This method

is popularly known as “cocktail watermarking”. Furthermore, the result shows

that the proposed method is robust against all possible watermarking attacks.

Zhao et al. [75] reported a dual domain-based method for image authentica-

tion. In their method, authors utilized the DCT for watermark generation and

DWT for watermark casting. Agreste and Andaloro [58] presented a novel DWT-

based watermarking method for any size of image which hides watermark into the

high-frequency sub-bands of DWT coefficient of host image. Later, Agreste and

Andaloro [59] reported another method which was the modified version of their

previous method by changing the filer bank by Daubechies-2, and concluded that

it is more robust to geometric, filtering, and StirMark attacks. Ghouti et al. [41]

selected balanced multi-wavelets filter bank for the data hiding and found that the

method is more robust against the various watermarking attacks. Furthermore,

Khelifi et al. [60] utilized the advantage of symlet-8 filter bank in their method.

Vahedi et al. [61] uses the Haar wavelet for message hiding. Moreover, Vahedi

et al. [42] exploited the advantage of symlet-4 filter bank to increase the quality

and robustness of watermarking method as compared to existing methods. In his

paper, they proposed a novel DWT-based method for color images by embedding

the binary watermark. For embedding the watermark, DWT-based methods use

three or higher level decompositions [39, 40, 42].

Steerable Pyramid Transform (SPT)-based Method

During the recent years, various image watermarking methods have been devel-

oped by using the DWT because it possess a number of desirable properties as

compared with the other transform-based methods. However, there are still rooms

for improvements in the field of watermarking. Although, DWT-based image wa-

termarking methods have various advantages, it suffers in terms of recording the

directional information which is very important components for any digital image

processing operations [44, 47]. Therefore, scholars in this area looking for an-

other reversible transform which possess all the properties of DWT. As a result,

researchers in this area propose various scale and directional image illustrations

during the years and results show that out of other representation, SPT having all

the advantages of DWT. Further, SPT is also capable in capturing the directional

information. Moreover, the results show that the SPT-based image watermarking

methods are more robust against the various geometric attacks in comparison with

19

DWT-based method [46]. Literature shows that the SPT keeps most of the ad-

vantages of DWT as its basis functions are confined to a small area in both space

and spatial-frequency. However, this recursive multi-scale & multi-directional de-

composition improve the drawbacks of DWT like; it is aliasing free and capable to

generate any number of orientation bands as it is based on a category of random

orientation filters produced by linear grouping of a set of basis filters [46, 76].

Invariance, multi-resolution, and capture of multi-scale and multi-resolution con-

structions in the images are some of main properties of SPT which make it superior

in watermarking methods. Moreover, researchers concluded on the basis of the re-

sults that SPT-based methods of image watermarking are more robust against

the common signal processing and geometric attacks [45, 46, 47, 63]. There are

lot of scope of the research using SPT-based watermarking method because very

few SPT-based image watermarking methods [46, 62] have been reported till date.

Drira et al. [46] developed a SPT-based method and shows that it is resistant

to JPEG compression, additive noise, and median filtering. Hossaini et al. [62]

presented a novel hybrid watermarking method using the SPT and singular value

decomposition (SVD) and concluded that proposed method has good visual qual-

ity and resistance against several attacks.

Following section reviews the various optimization methods used to improve

the performance of image watermarking along with their constraints.

1.6.3 Optimization Methods in Image Watermarking

Previous section presented a state-of-the-art survey of different image watermark-

ing methods using various domain like spatial and transform domain. Literature

survey reveals that the transform domain methods are more robust in compari-

son with spatial domain. In any watermarking method the aim is to maximize

the various parameters like transparency, robustness, capacity, etc. as given in

Section 1.3. Furthermore, the above mentioned parameters are the function of

strength factor of watermark to be added into the host image. However, all these

parameters are inversely related to each other. Therefore, to enhance the per-

formance of the method, researchers must select the optimum value of strength

factor of watermark, so that all above mentioned parameters are maximized simul-

taneously. From this point of view, there is a room for the optimization methods

in the watermarking area to enhance the performance by optimally selecting the

value of strength factor. There are various optimization methods namely; genetic

algorithm (GA), artificial bee colony (ABC), differential evolution (DE), particle

swarm optimization (PSO), etc. which have been used to increase the quality and

20

robustness of watermarking methods [77]. During the recent years, many water-

marking methods have exploited the advantages of various available optimization

methods [42, 55, 71, 78, 79, 80, 81, 82, 83, 84, 85]. The detailed survey of different

optimization methods has been presented by Karaboga and Akay [82] and Darwish

and Abraham [13]. A brief summary of the optimization methods used in image

watermarking methods is presented in Table 1.6.

Table 1.6: Categories of optimization methods in image watermarking.

S.No. Category Details

1. Genetic Algorithm Optimize 24 strength factor of watermark. [42]

Uses GA for search the optimum values of parameters. [81, 86]

Use GA to embed two bits of watermark data within each pixelof host image. [87]

2. Differential EvolutionMethod

Scale factors optimized using DE. [88]

Used a modified DE (self-adaptive differential evolution) algo-rithm for optimizing the scaling factors. [85]

Applied a DE optimization method, to search optimal scalingfactors. [89]

Use the DE to optimally design the quantization steps (QSs) forcalculating the strength of the watermark for achieving goodrobustness and quality. [90]

3. Artificial Bee ColonyMethod

Uses the ABC method to optimize pixel by pixel embeddingat different frequency sub-band with DWT, to improves theperformance of proposed image watermarking method. [91]

Genetic Algorithm

Many researchers used GA in their method for optimizing the watermark strength

to enhance the overall performance. GA have been used by Vahedi et al. [42, 80] to

compute the optimum strength of watermark and result shows that it improves the

robustness of existing methods. Kumsawat et al. [81, 86] used GA in their methods

for searching the optimum values of parameters and the embedding strength to

improve the transparency and robustness of proposed method. Mohammed et

al. [87] proposed a novel method in which they used GA to embed two bits of

watermark data within each pixel of host image.

Differential Evolution Method

A detailed survey of DE method has been presented by Das et al. [92]. Vester-

strom and Thomsen [93] compared the performance of DE with PSO, and other

21

evolutionary algorithms (EAs), and reported that DE outperforms other consid-

ered methods. Aslantas [88] reported a method in which the singular values (SVs)

of the host image are manipulated to embed the watermark by using multiple

scaling factors. Further, scale factors are optimized using DE to enhance the

performance of proposed method. Later, Aslantas [94] exploits the DE method

for optimizing the scaling factor parameters to achieve maximum robustness and

transparency. Ali and Ahn [85] used a modified DE (self-adaptive differential evo-

lution) algorithm for optimizing the scaling factors of watermark data to achieve

better robustness and quality in DWT-SVD based watermarking method. Fur-

ther, Ali et al. [89] applied a DE optimization method to search optimal scaling

factors to improve the quality of watermarked image and robustness of the wa-

termark. Lei et al. [90] proposed a method in which they used DE to optimally

design the quantization steps (QSs) for calculating the strength of the watermark

for achieving good robustness and quality.

Artificial Bee Colony Method

Artificial Bee Colony (ABC) optimization method is one of the most recently de-

veloped swarm-based algorithms. A very few watermarking methods have been

reported which utilizes the powerfulness of ABC for optimization in their methods

till date. Karaboga and Akay [82] compared ABC optimization method with GA,

PSO, etc. and concluded that the performance of ABC is better than other opti-

mization methods. Recently, Akay and Karaboga [95] presented a state-of-the-art

survey and applications in the field of image processing. Sha et al. [91] uses the

ABC method to optimize pixel by pixel embedding at different frequency sub-band

with DWT to improve the performance of proposed image watermarking method.

Following section reviews the various color spaces used in image watermarking

method in order to enhance the performance parameters along with their con-

straints.

1.6.4 Color Spaces in Image Watermarking Method

At the outset, early image watermarking methods embed the watermark message

within the gray scale or color host image in the form of bits or bit stream and

further these bit streams are replaced by some pictorial shape representations

[39, 40]. In various multimedia applications namely; MPEG-1, MPEG-2, and

other MPEGs, the color images are the basic component. Hence, it is vital to

develop a watermarking method for color images. Although digital images are

22

available in color format, most of the watermarking methods embed the gray

scale or binary image watermark while very few work has been reported for color

watermark [96, 97, 98]. In color image processing, we cannot ignore the effects of

different color spaces used on the performance of the method. Furthermore, Vahedi

et al. [99] demonstrated the effects of different color spaces on the performance

of an image watermarking method. The color spaces are dividing into two classes

namely; correlated and uncorrelated color space. The summary of color spaces

used in image watermarking is given in Table 1.7.

Table 1.7: Categories of color spaces in image watermarking method.

S.No. Category Comments

1. Correlated ColorSpaces

Uses HSV, RGB, YUV and HSI color models respectivelyin image watermarking method. [34, 58, 100, 101]

They uses HSI color space in DWT-based watermarkingmethod. [42]

2. Uncorrelated ColorSpaces

Uncorrelated color models Lab and Lαβ are used.[102]

Embeds the color watermark in Lab color space. [96]

Correlated Color Spaces

In a correlated color spaces, a color image is decomposed into three semi-independent

images in which change in one component may affect the other two components

of the images. In literature, various correlated color spaces namely; RGB, Y CbCr,

YIQ, HSI, HSV, etc. are being used in image watermarking methods by the re-

searchers. Methods proposed by researcher in [34, 58, 100, 101] uses HSV, RGB,

YUV, and HSI color models respectively. Vahedi et al. [99] reported a work

dedicated to the performance of DWT-based watermarking method on different

color spaces like RGB, Y CbCr, YIQ, HSI, and HSV. In which they concluded

that HSI color space outperforms other considered spaces. Golea et al. [97] pre-

sented the SVD-based RGB color image watermarking for embedding the color

RGB watermark into the RGB host image. Recently, Su et al. [98] presented QR

decomposition method for embedding the RGB color watermark into RGB host

image. Vahedi et al. [42] presented a DWT-based watermarking method in which

they used HSI color space to enhance the robustness and transparency.

Uncorrelated Color Spaces

Most of the image watermarking methods reported so far have used the correlated

color spaces. However, correlated color spaces impose the constraints to use only

23

one color channel at a time for casting the watermark data [103]. As a consequence,

researchers in this area proposed various color co-ordinate illustrations during the

recent years to removes the dependency of three decomposed images. Such class

of color space is called as uncorrelated color space. There exist some uncorrelated

color models such as Lab, Lαβ, uncorrelated color space (UCS), etc. which may

be used in color image watermarking to increase the robustness and quality by

using all the color image components of host and watermark images [43, 102,

104]. There is a lot of scope of the research using uncorrelated color spaces-

based watermarking method because very few work has been reported till date.

Chou and Wu [96] embedded the color watermark in Lab color space using less

computationally complex spatial-domain color image watermarking method.

1.7 Challenges in Image Watermarking

Though many image watermarking methods have been proposed and demon-

strated significant contribution, there are still some challenges which need to be

addressed. One of the main challenges of the watermarking problem is to achieve a

better trade-off between robustness, transparency, capacity, and security. In order

to address above mentioned issue (i.e. the trade-off) to achieve a better perfor-

mance, many researchers presented solutions for this issue in their work. However,

improvements are required to fulfill the expectation of the industry. This section

overviews some of the crucial challenges of image watermarking.

1.7.1 Use of Color Watermark

Most of the work during the past decade in this area have been reported for

protection of the color or gray scale image (host) by embedding the gray scale

or binary image watermark. To embed a binary or gray scale image, one has to

convert it from color image because in nature color images are available. However,

very few methods have been reported which embed color watermark for protection

of images [96, 97, 98]. From this point of view, there is still a lot of scope for

improvements in the field of image watermarking to embed a color watermark

into the color host image.

1.7.2 Color Spaces

In color image watermarking, we are dealing with color images for host and water-

mark. To read a color image there are various color spaces/ coordinates available

in the literature as given in Section 1.6.4. Therefore, researchers are use different

24

color spaces to improve the performance of the method. Robustness and trans-

parency of any method depend on how an input host is decomposed into three

color channels and also the decomposed images are independent with each other

or not. In other words, performance parameters of a color image watermarking

depend on the color space used in the method [99]. To consider above facts, there

is a lot scope to improve the performance of method by using a suitable color

space which decompose an image into three independent images. Fortunately, a

class of color spaces known as uncorrelated color space, generate three indepen-

dent images. However, a very few method has been reported which exploits this

class of color coordinate.

1.7.3 Transform Method

Though various reversible transform methods, as given in Section 1.6.2, have been

used in the reported image watermarking methods, still there is scope for improve-

ments of image watermarking methods by using some newly introduced transforms

(like SPT and other) which show better performance as compare to existing meth-

ods.

1.7.4 Optimization Method

A better trade-off is required between the parameters given in Section 1.3 to en-

hance the performance of the watermarking method. Furthermore, better trade-off

between the parameters depend on how optimally select the embedding param-

eters (like strength factor of watermark). To achieve this objective, there are

various optimization methods which have been used by the researchers as given

in Section 1.6.3. From this point of view, there is a room for improvements in

the performance of image watermarking method by using some newly developed

optimization methods like ABC, DE, etc.

1.7.5 3-D Watermarking

A recent challenge in watermarking is for protection of the 3-D objects or models

against the illegally utilizations which was introduced by Ohbuchi [105]. This

type watermarking is called “3-D watermarking”. Recently, 3-D watermarking

have been widely used in virtual reality, medical imaging, video games, computer

aided design, etc. This is considered as a new kind of multimedia that has scored

an increasing success. A very few work has been reported to protect the 3-D

models against the illegal utilizations till date, though it has been widely utilized

25

in the entertainment industry.

1.8 Scope of the Thesis

The challenges discussed above have not been fully resolved. Therefore, there is

a need to design and develop a robust image watermarking method. The main

contributions in this thesis are five-fold. It first aims to pre-process the input color

images (host and watermark) by transforming it into UCS color space. Second,

four efficient transform-based image watermarking methods for color images have

been proposed to enhance the transparency and robustness of method. Third,

three optimization methods have been used to improve the performance of pro-

posed methods by optimally selecting the strength factors of watermarks and then

post-processed the watermarked coefficients to reconstruct the watermarked im-

age. Fourth, applying various watermarking attacks in order to test the proposed

method on various benchmark/ validation parameters like composite-peak-signal-

to-noise ratio (CPSNR), structural similarity (SSIM), and normalized correlation

(NC). Finally, an image watermarking method is designed and developed using

the proposed methods for protection of color images.

Including this introductory chapter, the rest of the thesis is organized in the

following five chapters.

Chapter 2 discusses literature survey of the existing methods for image water-

marking using DWT which embed gray-scale watermark image for the protection

of color host images. It then briefly describes the various preliminaries like DWT

method, UCS, and GA followed by the mathematical concept of pre-processing of

input images, embedding, post-processing, and extraction of watermark images.

Moreover, GA has been used to optimize the watermark strength during the em-

bedding phase. Further, the testing of the proposed method is done against the

various watermarking attacks. The results of the proposed method are compared

with existing methods and finally discussion is presented.

Chapter 3 reviews of various available SPT-based image watermarking meth-

ods which used gray-scale watermark image for the protection of color host im-

ages. Then, brief descriptions of various preface such as SPT method, UCS, and

GA are presented followed by the mathematical concept of pre-processing of in-

put images, embedding, post-processing, and extraction watermark images. The

proposed method also uses GA to optimize the watermark strength during the em-

bedding phase. The results of the proposed method are compared with existing

methods and the method proposed in Chapter 2.

Chapter 4 deals with the protection of color images by embedding the color

26

watermark by using the DWT and ABC methods. This chapter presents a survey

of the existing methods for color image watermarking using DWT followed by

preliminaries. It then describes the pre-processing of input images, embedding,

post-processing, and extraction of watermark images. Then, three optimization

methods have been used to optimize the watermark strength during the embedding

phase followed by the testing of the method. The results of the proposed method

are compared with existing methods.

Chapter 5 proposes a color image watermarking method by casting the color

image watermark into the color host image by using the SPT and DE. A brief

discussion about the SPT and DE methods have been presented followed by the

procedure for embedding and extraction phase. In the proposed method, DE

has been used to optimize the watermark strength during the embedding phase

followed by the testing under various attacks. The results of the proposed method

are compared with existing methods and proposed method in Chapter 4.

The last chapter summarizes the key findings, main contributions of the

thesis, and possible scope for future research in this area.

27

Chapter 2

Digital Image Watermarking using Dis-

crete Wavelet Transform on Gray-Scale

Watermark Image

2.1 Introduction

Two embedding-domain have been used for digital image watermarking methods,

namely; spatial and transform domain as mentioned in Section 1.3. The detailed

survey related to the image watermarking methods is given in the Section 1.6. An

image watermarking method based on the transform domain is more robust than

the spatial domain [15]. Researchers suggested that DWT-based methods of image

watermarking are more robust against the common signal processing and malicious

attacks [51, 106, 107] as compared with the DFT and DCT-based methods. DWT-

based methods are capable in embedding a better quality of watermark and also

can recovered it from the watermarked image effectively. Therefore, this chapter

focuses on the protection of color images from its illegal utilization using a DWT-

based digital image watermarking method.

During past decade, many image watermarking methods [40, 41, 42, 58, 59,

60, 70, 71, 72, 73] have been reported which utilized the advantages of DWT over

the DFT and DCT [74]. DWT-based methods are more robust because it is more

close to the frequency model for the HVS [70]. The characteristics of HVS model

are used by various researcher in image watermarking to enhance the transparency

and robustness. Kundur et al. [39] exploit HVS model to generate a visual cover

for multi-resolution-based image watermarking method. Later, Reddy et al. [40]

and Ghouti et al. [41] uses the advantages of HVS in their methods. Furthermore,

the performance of the DWT-based methods depend on the parameters namely;

selection of filter bank, decomposition level, and selection of embedding decom-

posed coefficients [42]. Therefore, all the work reported in this area using DWT

are based on the changing the above mentioned parameters to show the better

robustness and transparency of watermarking method. Vahedi et al. [42] exploit

the advantage of symlet-4 filter bank to increase the quality and robustness of wa-

termarking method as compared to existing methods. In his paper, they proposed

a novel DWT-based method for color images by embedding the binary water-

mark. Vahedi et al. [42] showed that three level decomposition with all the four

sub-spaces namely approximation, horizontal, vertical, and diagonal along with

symlet-4 filter bank provides better results for image watermarking. Therefore, in

this method DWT has been used with three level of decomposition and symlet-4

filter bank for embedding the watermark. Moreover, to increase the quality and

robustness of watermarking methods, this method utilizes the capabilities of GA

to optimize the watermark strength.

Generally, the researchers used RGB, Y CbCr, YIQ, HSI, HSV, etc., color space

models for host image in their digital watermarking method. The above mentioned

color models are correlated i.e. the image components are not independent and

change in one component may affect the other components of the image. This

imposes the constraints for the researchers who used correlated color host image

to use only one color component at a time for embedding the watermark data.

However, there exist some uncorrelated color models such as Lab, Lαβ, UCS,

etc. [102, 108], which may be used in color image watermarking to increase the

robustness and quality by using all the color image components of host images.

Saraswat and Arya [108] used UCS for color transfer of images and observed that

UCS outperforms the other uncorrelated color spaces.

Therefore, due to the limitations of correlated color spaces and powerfulness of

DWT, this chapter proposes a DWT-based image watermarking method using un-

correlated color space (UCS). Further, GA has been used to optimize the strength

factor of the proposed watermarking method. In this chapter Section 2.2 presents

the various preliminaries used. Proposed method and performance improvements

using GA to the proposed method have been discussed in Section 2.3. Further,

experimental results and the conclusion of the chapter are presented in Section

2.4 and 2.5 respectively.

2.2 Preliminaries

In this chapter, the proposed method uses DWT, UCS and GA methods for digital

image watermarking. Therefore, following section describe the functions of these

methods in brief:

30

2.2.1 Discrete Wavelet Transform (DWT)

DWT is frequently used in various image processing applications namely; compres-

sion, watermarking, etc. DWT is a sampled version of continuous wavelet trans-

form. The main advantage of DWT is that it maintains both frequency and time

information at the same time which was missing in DFT. The transform based on

small sinusoidal waves of varying frequency and limited duration is called wavelet.

DWT is used to decompose the input image into sub-images of diverse spatial

direction (i.e. horizontal, vertical, and diagonal) and independent frequency area

[40, 41]. Transformation of an image from the spatial domain to DWT domain

by one level decomposes the input image into four different frequency bands in

which one is the low frequency and remaining three are the high frequency bands.

These bands are represented as LL (approximation detail of image), HL (hori-

zontal detail of image), LH (vertical detail of image), and HH (diagonal detail of

image) respectively. Magnitude of DWT coefficients is larger in the lowest bands

(LL) at each level of decomposition and is smaller for other bands (HH, LH, and

HL). Therefore, after one level decomposition, the further decomposition of given

image is done using only LL sub-space which is also decomposed into four distinct

frequency bands as mention above. Figure 2.1 shows the two dimensional image

of size 512×512 before and after the three level of DWT decomposition with their

sub-spaces size. Since, a two dimensional image has been used, it needed a 2-D

wavelet transform. The 2-D DWT is implemented as a 1-D row transform followed

by a 1-D column transform. The 2-DWT transform coefficients for input image

function f(n1, n2) of size N1 ×N2 are calculated using Eq. (2.1) and (2.2).

Wϕ(j0, k1, k2) =1√N1N2

N1−1∑n1=0

N2−1∑n2=0

f(n1, n2)ϕj0,k1,k2(n1, n2) (2.1)

W iψ(j0, k1, k2) =

1√N1N2

N1−1∑n1=0

N2−1∑n2=0

f(n1, n2)ψij0,k1,k2

(n1, n2) (2.2)

here, j0 is the starting scale and i = {H, V,D} indicates the directional index of

wavelet function. Eq. (2.1) calculates the approximation coefficient while (2.2)

calculates the other detail coefficients. 2-D scaling function ϕ and wavelet function

ψi, used in Eq. (2.1) and (2.2), may be calculated through the separable 1-D filter

having the impulse response hϕ(−n) and hφ(−n) respectively. Sub-spaces LL, HL,LH, and HH are calculated by putting the values of ϕ(n1, n2) and ψ

i(n1, n2) from

31

the Eq. (2.3)-(2.6) into Eq. (2.1) and (2.2).

ϕ(n1, n2) = ϕ(n1)ϕ(n2) (2.3)

ψH(n1, n2) = ψ(n1)ϕ(n2) (2.4)

ψV (n1, n2) = ϕ(n1)ψ(n2) (2.5)

ψD(n1, n2) = ψ(n1)ψ(n2) (2.6)

Figure 2.1: Three level decomposition layout of an image.

To reconstruct the image from the DWT coefficients, inverse DWT (IDWT) is

calculated as follows :

f(n1, n2) =1√N1N2

∑k1

∑k2

Wϕ(j0, k1, k2)ϕj0,k1,k2(n1, n2)

+1√N1N2

∑i=H,V,D

∞∑j=0

∑k1

∑k2

W iψ(j0, k1, k2)ψ

ij0,k1,k2(n1, n2)

(2.7)

To embed the watermark in DWT-based decomposed image, the transform coeffi-

cients of DWT are modified by watermark. Since, the low frequency band (LL) of

DWT decomposed image is similar to the original image, most of the information

or energy of original image lies in this frequency band. In order to maintain the

quality of watermarked image, this low frequency or approximation detail must be

preserved and maintained the robustness of embed watermark. Therefore, it is the

trade-off between the robustness and quality that at which extent the transform

coefficients are to be modified in order to optimize the overall method.

32

2.2.2 Uncorrelated Color Space (UCS)

The quality of the color image watermarking methods depends on how images

are split into three color channels, i.e. which color space was chosen. For better

quality, color space must be uncorrelated which makes the three color channels

semi-independent [103, 108] and may be used for embedding the watermark. This

method uses the recently developed UCS proposed by Liu [109]. UCS is derived

from RGB color space using principal component analysis (PCA). UCS uses a

linear transformation, WU ∈ R3×3, of the RGB color space to uncorrelate the

component images as shown in Eq. (2.8) [109];

⎡⎢⎣U(x, y)

C(x, y)

S(x, y)

⎤⎥⎦ = WU

⎡⎢⎣R(x, y)

G(x, y)

B(x, y)

⎤⎥⎦ (2.8)

The WU is calculated by factorizing the covariance matrix C using PCA in the

following form [109]:

C =W tUΛWU (2.9)

hereW tU and Λ are the orthogonal eigenvector matrix and diagonal eigenvalue ma-

trix with diagonal elements in a decreasing order, respectively. Saraswat and Arya

[108] used UCS for color transfer of images and observed that UCS outperforms

the other uncorrelated color spaces.

2.2.3 Genetic Algorithm (GA)

Genetic algorithm (GA) belongs to the class of evolutionary algorithms and is

an ingredient of artificial intelligence. These algorithms are capable to encode a

solution to the different engineering problems. Furthermore, to achieve a better

solution to the problem these algorithms use methods which are stimulated by

nature namely; inheritance, reproduction, mutation, and crossover. GAs were

reported by the Holland [110, 111].

Terminology

The term used in GA are as follows:

• Search space : the space of all possible solutions.

• Chromosome : it contains the solution in form of genes.

• Population : a set of individuals/chromosomes.

33

• Generation : the procedure of evaluation, reproduction, crossover and mu-

tation.

• Fitness : the value assigned to an individual based on how far or close it is

from the desired solution.

The pseudo-code of the GA is shown in Algorithm 2.1 and the flow chart is

depicted in Figure 2.2.

Algorithm 2.1 Genetic Algorithm

Genetic representation of solution to the problemCreate and initialize the population individuals;Evaluate the fitness of each individual in populationwhile Termination criteria is not satisfied do

1. Reproduction: Select the individuals with greater fitness for reproduction.2. Crossover: Reproduce new individuals through crossover.3. Mutation: Apply probabilistic alteration or modification on new individuals.4. Form a new population with these offsprings.

end while

Figure 2.2: Genetic Algorithm Flow Chart.

In GA, there are three operators: reproduction, crossover, and mutation. Ini-

tially, a population is generated randomly with uniform distribution followed by

reproduction, crossover, and mutation operators to generate a new population.

Offspring vector generation is a crucial step in GA process. The two operators,

34

crossover and mutation are used to generate the offspring vectors. The reproduc-

tion operator is used to select the best vector between offspring and parent for the

next generation. GA operators are explained briefly in the following sections.

Reproduction

Reproduction or selection operator selects the individuals or chromosomes to

crossover and to generate offsprings. The selection is crucial and important step

and it is based on the principle of the best one to be survive and generates new

offsprings. To select the best individual for the optimum solution of given prob-

lem, the fitness function is formulated. This function shows the closeness of a

current result to the desired result. There are various techniques for reproduction

operator namely; Roulette-wheel selection, tournament selection, rank selection,

steady-state selection, Boltzmann selection, and scaling selection.

Crossover

Crossover operator selects genes from parent chromosomes, combines them and

produces a new children or offspring. The idea behind this operator is that new

offspring may have better characteristics than both of the parents to achieve the

desired results.There are various techniques for crossover operator namely; single-

point crossover, two point crossover, uniform crossover, and arithmetic crossover.

Mutation

Mutation operator modifies or alter more than one gene values in an individual.

The advantage of the mutation operator is that it can produce new genes values

in the individual or chromosomes which is not present in a given search space.

Moreover, these new genes can produce the better solution for given problem.

2.3 Proposed Method

The proposed method explores the advantages of uncorrelated color space over

the correlated color space to improve the performance of watermarking methods

in terms of quality and robustness. It implants the gray-scale watermark image

into the color host image by modifying the decomposed wavelet coefficients of host

image. The watermark image is embedded in each color channel of host image to

increase the reliability during the recovery process and protect against the common

signal processing attacks. The proposed method consists of five phases namely;

preprocessing of host and watermark image, watermark embedding, image post

35

processing, extraction of watermark, and performance improvement using GA.

The basic structural design of the proposed method is shown in Figure 2.3. The

details of each phase of the proposed method is described in the following sections.

Figure 2.3: Structural design of proposed method.

Image Pre-processing

In image preprocessing, host RGB color image (H) is transformed into UCS color

space [109], using Eq. (2.10) which produces three independent image components

36

namely HU , HC , and HS.

⎡⎢⎣HU(x, y)

HC(x, y)

HS(x, y)

⎤⎥⎦ =WU

⎡⎢⎣HR(x, y)

HG(x, y)

HB(x, y)

⎤⎥⎦ (2.10)

The WU is calculated by factorizing the covariance matrix C using PCA in the

following form [109]:

C =W tUΛWU (2.11)

where W tU and Λ are the orthogonal eigenvector matrix and diagonal eigenvalue

matrix with diagonal elements in a decreasing order, respectively. The watermark

image is divided into 16 non-overlapping sub-areas as shown in Figure 2.3. These

sub-areas of watermark image are then re-arranged or scrambled by some pre-

defined sequence or key to introduce one more level of security and enforced the

user to use the key for the extraction of the image from watermarked image.

Further, third level of decomposition is applied on each component of host image

using symlet-4 wavelet function. The resultant third level wavelet coefficients,

Hk3, is chosen for embedding process, k = {LL,HL, LH,HH}.

Watermark Embedding

After preprocessing of the images, the scrambled watermark is embedded into the

DWT coefficients of host image. The third level decomposition coefficients of host

image are HLL3(x, y), HHL3(x, y), HLH3(x, y), and HHH3(x, y), representing the

approximation, horizontal, vertical, and diagonal details of 3-DWT decomposed

host image respectively. In order to increase the reliability and robustness of

proposed method against the malicious attacks, it is desired to hide the watermark

image in all the color components of host coefficients. Therefore, approximation

details of the third level coefficients are divided into 16 non-overlapping areas.

Now, the scrambled watermark is embedded into the approximation details of the

third level decomposed host image coefficients in its all color channels by using

Eq. (2.12).

HWk3(x, y) = Hk3(x, y) + α(r)Wp(x, y)

k = {LL,HL, LH,HH}p = {1, 2, 3, ..., 16}r = {1, 2, 3, ..., 16}

(2.12)

where, k represents the sub-areas of host image, p is the intended block of water-

mark, and α denotes the strength of modification done in the host coefficients.

37

Post-processing

After embedding the watermark image into corresponding DWT host image co-

efficients, inverse DWT (IDWT) is taken for watermarked host image coefficients

which returns watermarked image in UCS color space. Finally UCS watermarked

image is reconverted into RGB color space.

Watermark Extraction

The watermark extraction process requires the watermarked image and the pre-

defined key. To extract the watermark, RGB watermarked image is transformed

into UCS color space and then 3-DWT decomposition using symlet-4 wavelet filter

bank on each channel is applied. To gathered the fraction watermarked image,

a reverse process of embedding ia applied as shown in Figure 2.3 and extract

the watermark image from this fraction using Eq. (2.13). Finally, the scrambled

watermark image is rearranged to its original sequence.

Wp(x, y) =Hk3(x,y)−HWk3

(x,y)

α(r)

k = {LL,HL, LH,HH}p = {1, 2, 3, ..., 16}r = {1, 2, 3, ..., 16}

(2.13)

Performance Improvement using GA

The watermarked image HWk3 is subjected to various watermarking attacks which

degrade the performance of watermarking methods. There are mainly two param-

eters namely; quality and robustness which must be maximized for a watermarking

method, but these parameters are inversely related with each other i.e. if qual-

ity increases, robustness suffers and vice-versa. In this method, optimum values

of these parameters are dependent on the suitable values of strength factor (α).

Therefore, this method uses GA optimization method for selecting the values of

α which optimizes the quality and robustness in terms of fitness function. There

are 16 strength factors used in this method whose optimized values are calculated

38

by minimizing the following fitness function using GA [42].

Fitness =100

CPSNR+ 2×

90%∑Q=60%

[1−NCjpg(Q)]

+50%∑

Q=10%

[1−NCjpg(Q)] +3∑i=1

[1−NCfilter(i)]

+

3∑i=1

[1−NCscale(i)] +

3∑i=1

[1−NCnoise(i)]

+

4∑i=1

[1−NCcrop(i)] + [1−NCrotation]

(2.14)

where, CPSNR is composite peak signal-to-noise ratio, and NC is normalized

correlation. These vales are calculated by applying the mentioned attacks on wa-

termarked image. The GA method is executed for 200 iterations to calculate the

optimum values of strength factors (α).

2.4 Experimental Results

To compare the performance of proposed and considered image watermarking

methods, two popularly used 24-bit RGB color images namely lena and mandrill

each of size 512× 512 are used as host images and two gray scale images namely;

RTU logo and aeroplane image each of size 64× 64 are used as watermark images

to embed them in the host images and are shown in Figure 2.4. All the considered

images have been taken from USC-SIPI image database [112] except RTU logo

which is taken from Rajasthan Technical University, Kota, India.

(a) Lena (b) Mandrill (c) RTU logo (d) Aeroplane

Figure 2.4: (a)-(b). RGB host images and (c)-(d). Gray scale watermark images.

In the pre-processing step of the proposed method, host images are converted

into UCS color space followed by third level decomposition of host images and

scrambling of watermark images. One of the possible scrambled watermark image

39

is shown in Figure 2.3. Now, the watermark images are embedded into each of the

considered host images.

The performance of the proposed methods is compared with Vahedi et al. [42] who

also used DWT with correlated color spaces and exploits the powerfulness of GA to

increases the quality and robustness of image watermarking method. To compare

the objective test for quality measures of resultant watermarked images using

proposed method and considered methods, CPSNR [42] performance parameters

are calculated and depicted in Table 2.1. From Table 2.1, it is validated that

both the methods maintain the quality of the watermarked image in terms of

CPSNR (> 35dB). However, the proposed method shows lower values of CPSNR

as compared to other considered method due to the hiding of complete watermark

more than once and into the approximation detail of host image.

Table 2.1: Comparison of CPSNR values of watermarked images resultant fromproposed and considered method.

S.No. Used Watermark Watermarkedimages

Vahedi et al. [42] ProposedMethod

1. RTU Logo Lena 36.74 35.92

2. RTU Logo Mandrill 35.85 35.67

3. Aeroplane Lena 36.32 35.61

4. Aeroplane Mandrill 35.42 35.59

The robustness of the proposed method has been tested by applying different

attacks on the watermarked images. This method consists of filtering (mean,

median, wiener), noise (gaussian, poisson, salt and pepper), JPEG compression,

rotation, scaling, and cropping attacks. The attacks are applied on all the four

watermarked images embedded with RTU logo and aeroplane watermark images.

The comparison of NC values after applying the attacks on watermarked images

embedded with RTU logo and aeroplane image are depicted in Table 2.2. From

Table 2.2, it is observed that the robustness of watermarked images embedded

with RTU logo have higher values of NC as compared to aeroplane image due

to the coarseness of aeroplane image as compared to RTU logo. Moreover, the

similar performance of the proposed method can be observed from Figure 2.5 and

Figure 2.6 where the extracted watermark images are RTU logo and Aeroplane

images respectively, for all the considered attacks. Therefore, it is validated from

the results that the proposed method produces high quality and better robust

watermarked images and can be utilized for content authentication to protect the

copyrighted images. The comparative results show that the proposed method

outperforms other method for all the considered attacks.

40

Tab

le2.2:

Com

parison

ofrobustnessin

term

sof

NC

values

obtained

afterap

plyingattackson

thewatermarkedim

ages.

RTU

Logo

AeroplaneIm

age

lena

Mandrill

lena

Mandrill

S.N

o.

Attacks

Vahedi

et

al.

[42]

Propose

dM

eth

od

Vahedi

et

al.

[42]

Propose

dM

eth

od

Vahedi

et

al.

[42]

Propose

dM

eth

od

Vahedi

et

al.

[42]

Propose

dM

eth

od

1Med

ianfiltering(3

×3)

0.73

0.76

0.71

0.75

0.70

0.75

0.67

0.74

2W

iener

filtering(3

×3)

0.88

0.92

0.85

0.90

0.84

0.91

0.81

0.89

3Meanfiltering(3

×3)

0.70

0.99

0.68

0.97

0.67

0.98

0.65

0.96

4Gaussiannoise(0.006)

0.83

0.87

0.81

0.85

0.79

0.86

0.77

0.84

5Poissonnoise

0.84

0.88

0.82

0.86

0.80

0.87

0.77

0.85

6Salt

andpep

per

noise

0.92

0.96

0.89

0.94

0.87

0.95

0.85

0.93

7JPEG

compression(10%)

0.68

0.71

0.66

0.70

0.65

0.70

0.63

0.69

8JPEG

compression(90%)

0.84

0.88

0.82

0.86

0.80

0.87

0.77

0.85

9Rotation(1

0)

0.92

0.96

0.89

0.94

0.87

0.95

0.85

0.93

10

Scaling(0.6)

0.94

0.98

0.91

0.96

0.89

0.97

0.87

0.95

11

Scaling(0.9)

0.95

0.99

0.92

0.97

0.90

0.98

0.88

0.96

12

Scaling(1.2)

0.95

0.99

0.92

0.97

0.90

0.98

0.88

0.96

13

Cropping(10%)

0.92

0.96

0.89

0.94

0.87

0.95

0.85

0.93

14

Cropping(20%)

0.81

0.84

0.79

0.83

0.77

0.84

0.75

0.82

15

Cropping(30%)

0.71

0.74

0.69

0.73

0.68

0.73

0.66

0.72

16

Cropping(40%)

0.59

0.62

0.58

0.61

0.56

0.61

0.55

0.60

41

Extracted RTU logo Watermark Image After Attacks

Median (3x3) Wiener (3x3) Mean (3x3) Gaussian (0.006)

JPEG (10%) JPEG (90%) Poisson Salt & Pepper

Rotation (10) Scaling (0.6) Scaling (0.9) Scaling (1.2)

Cropping (10%) Cropping (20%) Cropping (30%) Cropping (40%)

Figure 2.5: Extracted RTU logo watermarks by proposed method after applyingconsidered attacks.

42

Extracted Aeroplane Watermark Image After Attacks





Figure 2.6: Extracted Aeroplane image watermarks by proposed method afterapplying considered attacks.

43

2.5 Results and Discussions

This chapter proposes a DWT-based image watermarking method for protecting

the color host image by embedding gray-scale image using UCS and GA. The

use of UCS color space increases the effective utilization of all color channels

of host image which is not feasible in correlated color spaces while GA is used

for optimizing the strength factors to improve the quality and robustness of the

proposed method. The results validate that the proposed method is better than

other method for all the considered performance parameters. In other words, the

UCS color space is outperforms than the correlated color space utilized by Vahedi

et al. [42]. Therefore, it is concluded that the proposed method has high quality

and robust results and can further be used for protection of the copyrighted images.

The next chapter uses the SPT-based method to embed the gray-scale water-

mark image into color host image.

44

Chapter 3

Digital Image Watermarking using Steer-

able Pyramid Transform on Gray-Scale

Watermark Images

3.1 Introduction

Previous chapter presented an image watermarking method, which is based on

DWT and further GA has been exploited to optimize the performance of method.

In addition, the proposed method has been compared with existing method and

results shows that the proposed method outperforms. Although, proposed method

in previous chapter working well, there is still room for improvements in the field

of image watermarking by applying some latest developed transform as mentioned

in section 1.6.1.

The imperfect ability of DWT in capturing directional information, which is neces-

sary components for image perception, processing, and reconstruction [47], causes

researcher to find alternative transform having all the advantages of DWT and

simultaneously removes all disadvantages. Fortunately, researcher come up sev-

eral scale and directional image illustrations and results show that out of other

representation SPT outperforms among the class [46]. Researchers suggested that

SPT-based methods of image watermarking are more robust against the common

signal processing and geometric attacks [45, 46, 47, 63]. Literature shows that the

SPT keeps most of the advantages of DWT as its basis functions are confined to

a small area in both space and spatial-frequency. However, this recursive multi-

scale & multi-directional decomposition improve their drawbacks like; it is aliasing

free and capable to generate any number of orientation bands as it is based on

a category of random orientation filters produced by linear grouping of a set of

basis filters [46, 76]. Invariance, multi-resolution, and capture of multi-scale and

multi-resolution constructions in the images are some of main properties of SPT

which make it superior in watermarking methods.

Therefore, in this work SPT has been used with three level of decomposition

in place of DWT for embedding the watermark. Moreover, GA method is also

used for optimizing the strength factor of watermark to get better quality and

robustness as used in the previous method discussed in Chapter 2. In the similar

fashion of previous method described in Chapter 2, the host images and watermark

images have been converted to UCS color space before embedding.

Rest of the chapter is organized as follows. Section 3.2 presents the SPT used

in the proposed method while GA and UCS have already been described in Section

2.2. In Section 3.3, proposed method has been discussed followed by experimental

results and conclusions in the successive sections.

3.2 Steerable Pyramid Transform (SPT)

The earliest successful multi-scale, multi-orientation decomposition of an image

is SPT which has been proposed by Freeman and Adelson [44, 45]. The SPT

is a wavelet-like illustration whose investigation functions are scaled and rotated

editions of a single directional wavelet. Steerability is the property in which funda-

mental wavelets can be rotated to any orientation by appearing appropriate linear

groups of a primary set of equiangular directional wavelet components [113]. This

property is utilized in the SPT for finding the direction of basis function of an

image [76]. SPT decomposes the input image into a set of sub-bands of a variety

of orientations. Figure 3.1 depicts the decomposition block diagram of an input

image, consisting of the three kind of filters; low-pass (L0), high-pass (H0), and

bank of band-pass (B0, ..., Bk) filters. Here k is the order of basis functions of the

steerable pyramid and k + 1 are the orientations. The input image is sub-divided

into the high and low-pass sub-bands by the high and low pass filters respectively.

Every low-pass sub-band is again sub divided into the k + 1 oriented sub-bands

and a low pass sub-band. Finally, sub-sampled are created by a factor 2 and then

further decomposition is performed.

3.3 Proposed Method

The proposed method uses the uncorrelated color space to improve the quality

and robustness of watermarking methods. This method embeds the gray scale

watermark image into the color host image by modifying the decomposed SPT

coefficients of host image. The watermark is added in each color channel of host

image which increases the reliability of extraction process and protection against

46

Figure 3.1: Block diagram for steerable pyramid decomposition of an image.

the common signal processing attacks. The proposed method is divided into five

phases namely, preprocessing of host and watermark images, watermark embed-

ding, image post processing, extraction of watermark, and performance improve-

ment using GA. Figure 3.2 shows basic structural design of the proposed method.

The details of each phase of the proposed method is described in the following

sections.


Image preprocessing, changes the host RGB color image (H) into UCS color image

[109], using Eq. (3.1) and generates three independent image components namely

HU , HC , and HS.

⎡⎢⎣HU(x, y)

HC(x, y)

HS(x, y)

⎤⎥⎦ =WU

⎡⎢⎣HR(x, y)

HG(x, y)

HB(x, y)

⎤⎥⎦ (3.1)

here, WU is calculated by factorizing the covariance matrix C using principal

component analysis (PCA) in the following form [109]:

C =W tUΛWU (3.2)

47


where W tU and Λ are the orthogonal eigenvector matrix and diagonal eigenvalue

matrix with diagonal elements in a decreasing order, respectively. Further, 16

non-overlapping sub-areas are generated from watermark as shown in Figure 3.2.

These sub-areas of watermark are then re-arranged or scrambled by some pre-

defined sequence or key. This increases the level of security and enforces the user

to use the key for the extraction of the watermarked image. Further, third level

of decomposition is applied on each component of host image. The resultant third

48

level SPT coefficients, Hk3, is chosen for embedding process, k = {1, 2, 3, ..., 16}.

Watermark Embedding

After the preprocessing step, the scrambled watermark is embedded into the SPT

coefficients of host image (Hk3). In order to increase the reliability and robust-

ness of proposed method against the malicious attacks, it is desired to hide the

watermark image in all the color components of host coefficients. Therefore, low

frequency details of the third level coefficients are divided into 16 non-overlapping

areas. Now, the scrambled watermark is embedded into the low frequency details

of the third level decomposed host image coefficients in its all color channels by

using Eq. (3.3).


k = {1, 2, 3, ..., 16}p = {1, 2, 3, ..., 16}r = {1, 2, 3, ..., 16}

(3.3)

where, k represents the sub-areas of host image, p is the intended block of water-


Post-processing

After embedding the watermark image into corresponding SPT host image coeffi-

cients, inverse SPT (ISPT) is taken for watermarked host image coefficients which

returns watermarked image in UCS color space. Finally UCS watermarked image

is reconverted into RGB color space.


The watermark extraction process needs the watermarked image and the pre-

defined key, to extract the watermark. RGB watermarked image is transformed

into UCS color space and then SPT decomposition on each channel is applied.

To gathered the watermarked image, a reverse process of embedding is applied

as shown in Figure 3.2 and extract the watermark using Eq. (3.4). Finally, the

scrambled watermark is rearranged to its original sequence using the pre-defined

49

key.


(x,y)

α(r)

k = {1, 2, 3, ..., 16}p = {1, 2, 3, ..., 16}r = {1, 2, 3, ..., 16}

(3.4)

Performance Improvement using GA

The watermarked image is subjected to various watermarking attacks which de-

grade the performance of watermarking methods such as quality and robustness

which must be maximized for a watermarking method, but these parameters are

inversely related with each other i.e. if quality increases, robustness suffers and

vice-versa. In this method, optimum values of these parameters are dependent on

the suitable values of strength factor (α). Therefore, this method also uses GA

optimization method for selecting the values of α which optimizes the quality and

robustness in terms of fitness function as described in 2.3.


To compare the performance of proposed and considered image watermarking

methods, two popularly used 24-bit RGB color images namely, lena and mandrill

each of size 512× 512 are used as host images and two gray scale images namely,

RTU logo and aeroplane image each of size 64× 64 are used as watermark images

to embed them in the host images and are shown in Figure 3.3. All the considered



(a) Lena (b) Mandrill (c) RTU logo (d) Aeroplane

Figure 3.3: (a)-(b). RGB host images and (c)-(d). Gray scale watermark images.

The performance of the proposed method is compared with method proposed

in Chapter 2 in which DWT and GA have been used to increases the quality

and robustness of image watermarking method. To compare the objective test

50

for quality measures of resultant watermarked images using proposed method and

considered methods, CPSNR [42] performance parameters are calculated and de-

picted in Table 3.1. From Table 3.1, it is validated that all the methods maintain

the quality of the watermarked image in terms of CPSNR (> 35dB). However,

the proposed method shows better results in terms of CPSNR as compared to

proposed DWT-based method.

Table 3.1: Comparison of CPSNR values of watermarked images resultant fromproposed and considered methods.

S.No. UsedWatermark

Watermarkedimages

ProposedDWT-basedmethod[Chapter 2]

Proposed SPT-based Method

1. RTU Logo Lena 35.92 37.23

2. RTU Logo Mandrill 35.67 36.87

3. Aeroplane Lena 35.61 37.11

4. Aeroplane Mandrill 35.59 36.08

The robustness of the proposed methods has been tested by applying different

attacks on the watermarked images. This chapter consists of filtering (mean,

median, wiener), noise (gaussian, poisson, salt and pepper), JPEG compression,

rotation, scaling, and cropping attacks. The attacks are applied on all the four

watermarked images embedded with RTU logo and aeroplane watermark images.

The comparison of NC values after applying the attacks on watermarked images

embedded with RTU logo and aeroplane image are depicted in Table 3.2. From

Table 3.2, it is observed that the robustness of watermarked images embedded

with RTU logo have higher values of NC as compared to aeroplane image due to

the coarseness of aeroplane image as compared to RTU logo. Moreover, it can be

concluded from Figure 3.4 and Figure 3.5 which shows the extracted RTU logo

and Aeroplane watermarks by proposed method respectively, for all the considered

attacks have been depicted. Therefore, it is validated from the results that the

proposed method using SPT produces high quality and better robust watermarked

images and can be utilized for content authentication to protect the copyrighted

images. The comparative results show that the proposed method outperforms

other methods for all the considered attacks.


This chapter introduces a SPT-based image watermarking method using UCS and

GA. The uncorrelated color space increases the effective utilization of all color

51

Tab

le3.2:

Com

parison

ofrobustnessin

term

sof

NC

values

obtained

afterap

plyingattackson

thewatermarkedim

ages.

RTU

Logo

AeroplaneIm

age

lena

Mandrill

lena

Mandrill

S.N

o.

Attacks

Propose

dDW

T-

base

dm

eth

od

[Chapte

r2]

Propose

dSPT-b

ase

dM

eth

od

Propose

dDW

T-

base

dm

eth

od

[Chapte

r2]

Propose

dSPT-b

ase

dM

eth

od

Propose

dDW

T-

base

dm

eth

od

[Chapte

r2]

Propose

dSPT-b

ase

dM

eth

od

Propose

dDW

T-

base

dm

eth

od

[Chapte

r2]

Propose

dSPT-b

ase

dM

eth

od

1Med

ianfiltering(3

×3)

0.76

0.79

0.75

0.80

0.75

0.78

0.74

0.74

2W

iener

filtering(3

×3)

0.92

0.93

0.90

0.90

0.91

0.92

0.89

0.91

3Meanfiltering(3

×3)

0.99

0.98

0.97

0.98

0.98

0.98

0.96

0.97

4Gaussiannoise(0.006)

0.87

0.91

0.85

0.86

0.86

0.87

0.84

0.87

5Poissonnoise

0.88

0.94

0.86

0.90

0.87

0.88

0.85

0.90

6Salt

andpep

per

noise

0.96

0.97

0.94

0.96

0.95

0.96

0.93

0.94

7JPEG

compression(10%)

0.71

0.72

0.70

0.71

0.70

0.71

0.69

0.70

8JPEG

compression(90%)

0.88

0.88

0.86

0.88

0.87

0.90

0.85

0.86

9Rotation(1

0)

0.96

0.97

0.95

0.96

0.95

0.97

0.93

0.94

10

Scaling(0.6)

0.98

0.98

0.96

0.98

0.97

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Scaling(0.9)

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Scaling(1.2)

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Cropping(10%)

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Cropping(20%)

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Cropping(30%)

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Cropping(40%)

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52






Figure 3.4: Extracted RTU logo watermarks by proposed method after applyingconsidered attacks.

53






Figure 3.5: Extracted Aeroplane image watermarks by proposed method afterapplying considered attacks.

54

channels of host image as compared to correlated color spaces while GA optimizes

the strength factors and improves the quality and robustness of the proposed

method. The results validates that the proposed method using SPT and UCS

is better than existing methods which are based on DWT for all the considered

performance parameters. Hence, it is concluded that SPT-based method using

UCS outperforms as compared with the DWT.

The next two chapters describe the watermarking methods for embedding the

color watermark images into color host images.

55

Chapter 4

Digital Image Watermarking using Dis-

crete Wavelet Transform on Color Wa-

termark Images

4.1 Introduction

Previous two chapters (2 and 3) present the image watermarking method for the

protection of color host images by embedding gray-scale images using the DWT

and SPT, respectively. Further, the capabilities of GA has been utilized in both

the presented methods to enhance the performance by optimally selecting the

strength factors as discussed in the previous chapters. Finally, it is validates by

the analysis of experimental results that out of both the proposed methods, the

SPT-based method using UCS outperforms among the class. Furthermore, all

the researchers are trying to develop a color image watermarking by embedding

the color watermark images into the color host images, since all the images are

available in the color format. Hence, this chapter introduces a novel DWT-based

color image watermarking method for protection of color images by embedding

the color watermark. Further, this method uses the powerfulness of ABC method

for optimizing the various embedding parameters.

DWT-based image watermarking methods are capable to embed a fairly good

quality of watermark and it can recover the watermark from watermarked image

effectively. The quality and robustness of DWT-based methods depend on the

selection of particular filter bank and decomposition level. Agreste and Andaloro

[58] developed a DWT-based method for any size of image which implants water-

mark data into high-frequency sub-bands of DWT coefficient. This watermarked

data is imperceptible as per HVS directions. Later, Agreste and Andaloro [59]

modified the previous method by changing the filer bank by Daubechies-2 and ob-

served that it is more robust to geometric, filtering, and StirMark attacks with a

low rate of false alarm. Ghouti et al. [41] selected balanced multi-wavelets for the

data hiding and found that the method is more robust against the standard water-

marking attacks. Vahedi et al. [42] exploited the advantage of symlet-4 filter bank

to increase the quality and robustness of watermarking method as compared to

existing methods. For embedding the watermark, DWT-based methods use three

or higher level decompositions [39, 40, 42]. Vahedi et al. [42] showed that three

level decomposition with all the four sub-spaces namely; approximation, horizon-

tal, vertical, and diagonal along with symlet-4 filter bank provide better results for

image watermarking. Therefore, in this method, DWT has been used with three

level of decomposition and symlet-4 filter bank for embedding the watermark.

Moreover, to increase the quality and robustness of watermarking methods,

researchers also used many optimization methods like GA, PSO, ABC, differential

evolution (DE), etc. [42, 55, 78, 81, 82]. PSO and GA have been used by Vahedi

et al. [42, 80] to calculate the optimized strength of watermark which improves

the robustness of existing methods. Therefore, in this chapter, the strength factor

of the proposed method has been optimized using three optimization function

namely; GA, ABC, and DE and their performance are compared.

Initially, image watermarking methods hide the watermark message into gray

scale or color host image in the form of bits or bit stream, and later these digital

bit streams are replaced by some pictorial shape like image [39, 40]. Generally,

various watermarking methods embed the gray scale or binary image watermark

while very few work has been reported for color watermark [96, 97, 98], though

most multimedia images are available in color. Generally, the researchers used

RGB, Y CbCr, YIQ, HSI, HSV, etc. color space models for both watermarks and

host images in their digital watermarking method. Golea et al. [97] proposed

the SVD-based RGB color image watermarking for embedding the color RGB

watermark into the RGB host image. Recently, Su et al. [98] presented QR

decomposition method for embedding the RGB color watermark into RGB host

image. In his paper, they concluded that the proposed method is robust against

the attacks such as compression, filtering, cropping, etc. The above mentioned

color models are correlated i.e. the image components are not independent and

change in one component may affect the other components of the image. This

imposes the constraints for the researchers to use only one color component at a

time for embedding the watermark data. However, there exist some uncorrelated

color models such as Lab, Lαβ, etc. [43, 102, 104] which may be used in color image

watermarking to increase the robustness and quality by using all the color image

components of host and watermark images. Chou and Wu [96] embedded the color

58

watermark in Lab color space using less computationally complex spatial-domain

color image watermarking method. However, the robustness of this method is

poor.

Therefore, due to the limitations of correlated color spaces, rare use of colored

watermark images, and powerfulness of DWT, this chapter proposes a novel DWT-

based color image watermarking method using UCS. Further, ABC has been used

to optimize the strength factor of the proposed watermarking method.

Rest of the chapter is organized as follows. Section 4.2 and 4.3 describes the

working of ABC and DE methods respectively, used in this method. GA, DWT,

and UCS are already been described in Section 2.2 of Chapter 2. The proposed

method is presented in Section 4.4. Section 4.5 describes the method validation

parameters and experimental results are discussed in Section 4.6. Finally, Section

4.7 concludes the chapter.

4.2 Artificial Bee Colony (ABC) Method

ABC [82] is a newly developed swarm intelligence-based method which is inspired

by the intelligent food foraging behavior of honey bees. Similar to the other

population-based methods, ABC solution search process is an iterative process.

After, initialization of the ABC parameters and swarm, it requires the repetitive

iteration of the three phases namely; employed bee phase, onlooker bee phase,

and scout bee phase. The initialization of the swarm and details of each phase are

described in the following sections.

Initialization of the Swarm

The parameters for ABC are the number of food sources, number trials after which

a food source is considered to be abandoned, and termination criteria. In the basic

ABC, the number of food sources is equal to the employed bees or onlooker bees.

Initially, a uniformly distributed initial swarm of SN food sources, where each

food source xi(i = 1, 2, ..., SN) is a D-dimensional vector, are generated. Here

D is the number of variables in the optimization problem and xi represent the

ith food source in the swarm. Each food source is generated as mentioned in Eq.

(4.1) [82].

xij = xminj + rand[0, 1](xmaxj − xminj) (4.1)

here, xminj and xmaxj are bound of xi in jth direction and rand[0, 1] is a uniformly

distributed random number in the range [0, 1].

59

Employed Bee Phase

In employed bee phase, employed bees modify the current solution (food source)

based on the information of individual experience and the fitness value of the new

solution. If the fitness value of the new solution is higher than that of the old

solution, the bee updates her position with the new one and discards the old one.

The position update equation for ith candidate in this phase is presented in Eq.

(4.2) [82].

vij = xij + φij(xij − xkj) (4.2)

here, k ∈ {1, 2, ..., SN} and j ∈ {1, 2, ..., D} are randomly chosen indices. k must

be different from i. φij is a random number between [−1, 1].

Onlooker Bees Phase

After completion of the employed bees phase, the onlooker bees phase starts.

In onlooker bees phase, all the employed bees share the new fitness information

(nectar) of the new solutions (food sources) and their position information with

the onlooker bees in the hive. Onlooker bees analyze the available information and

select a solution with a probability, probi, related to its fitness. The probability

probi may be calculated using Eq. (4.3) [82].

probi =fitnessi∑SNi=1 fitnessi

(4.3)

here, fitnessi is the fitness value of the solution i. As in the case of the employed

bee, it produces a modification on the position in its memory and checks the fitness

of the candidate source. If the fitness is higher than that of the previous one, the

bee memorizes the new position and forgets the old one.

Scout Bees Phase

If the position of a food source is not updated up to predetermined number of

cycles, then the food source is assumed to be abandoned and scout bees phase

starts. In this phase, the bee associated with the abandoned food source becomes

scout bee and the food source is replaced by a randomly chosen food source within

the search space. In ABC, predetermined number of cycles is a crucial control

parameter which is called limit for abandonment. Assume that the abandoned

source is xi. The scout bee replaces this food source by a randomly chosen food

60

source which is generated as given in Eq. (4.4) [82].

xji = xjmin + rand[0, 1](xjmax − xjmin), for j ∈ {1, 2, ..., D} (4.4)

here, xminj and xmaxj are bound of xi in jth direction. The pseudo-code of the

ABC is shown in Algorithm 4.1 [82]. In this method, ABC is being used to

optimize the watermarking parameters for increasing the quality and robustness

of the proposed method.

Algorithm 4.1 Artificial Bee Colony Algorithm

Initialize the parameters;while Termination criteria is not satisfied do

1. Employed bee phase for generating new food sources.2. Onlooker bees phase for updating the food sources depending on their nectar

amounts.3. Scout bee phase for discovering the new food sources in place of abandoned

food sources.4. Memorize the best food source found so far.5. If a termination criteria is not satisfied, go to step 1; otherwise output the best

solution found so far.end while

4.3 Differential Evolution (DE) Algorithm

DE algorithm is relatively a simple, fast, and population-based stochastic search

method [114] which falls under the category of Evolutionary Algorithms (EAs).

However, it differs significantly from EAs, e.g. in EAs, crossover is applied first

to generate a trial vector, which is then used within the mutation operation to

produce one offspring while, in DE, crossover follows the mutation operator [115].

DE has several methods of selecting the target vector, number of difference vec-

tors, and the type of crossover [114]. This paper uses DE/rand/1/bin scheme in

which DE stands for differential evolution, ‘rand’ specifies that the target vector

is selected randomly, ‘1’ is for number of differential vectors, and ‘bin’ notation is

for binomial crossover. The popularity of DE is due to its applicability to a wider

class of problems and ease of implementation. The detailed description of DE is

as follows:

Like other population-based search methods, DE also searches the solution

using a population of potential solutions (individuals). In a D-dimensional search

space, an individual is represented by a D-dimensional vector (xi1, xi2, ..., xiD)

where i = 1, 2, ..., NP and NP is the population size (number of individuals).

61

In DE, there are three operators: mutation, crossover, and selection. Initially, a

population is generated randomly with uniform distribution followed by mutation,

crossover, and selection operators to generate a new population. Offspring vector

generation is a crucial step in DE process. The two operators (mutation and

crossover) are used to generate the offspring vectors. The selection operator is

used to select the best vector between offspring and parent for the next generation.

DE operators are explained briefly in the following sections.

Mutation

A trial vector is generated by the DE mutation operator for each individual of

the current population. For generating the trial vector, a target vector is mutated

with a weighted differential. An offspring is produced in the crossover operation

using the newly generated trial vector. If G is the index for generation counter,

the mutation operator for generating a trial vector ui(G) from the parent vector

xi(G) is defined as follows:

• Select a target vector, xi1(G), from the population, such that i �= i1.

• Again, randomly select two individuals, xi2 and xi3 , from the population

such that i �= i1 �= i2 �= i3.

• Then the target vector is mutated for calculating the trial vector as follows:

ui(G) = xi1(G) + F ×Variation Component︷︸︸︷(xi2(G)− xi3(G))︸︷︷︸Step size

(4.5)

here F ∈ [0, 1] is the mutation scale factor which is used for controlling the

amplification of the differential variation [115].

Crossover

Offspring x′i(G) is generated using the crossover of parent vector (xi(G)) and the

trial vector (ui(G)) as follows:

x′ij(G) =

⎧⎨⎩uij(G), if j ∈ J

xij(G), otherwise.(4.6)

here J is the set of crossover points or the points that will go under perturbation

and xij(G) is the jth element of the vector xi(G).

62

Different methods may be used to determine the set J of crossover points in

which binomial crossover and exponential crossover are the most frequently used

[115]. In this chapter, the DE and its variants are implemented using binomial

crossover whereas for a D dimensional problem, the crossover points are randomly

selected from the set of possible points, {1, 2, . . . , D}. Algorithm 4.2 shows the

steps of binomial crossover to generate crossover points [115].

Algorithm 4.2 Binomial CrossoverLet CR represents the probability with which the considered crossover points will be included.U(1,D) is a uniformly distributed random integer between 1 and D.J = φj∗ ∼ U(1, D);J ← J ∪ j∗;for each j ∈ 1...D do

if U(0, 1) < CR and j �= j∗ thenJ ← J ∪ j;

end ifend for

Selection

There are two functions for the selection operator: First, it selects the individual

for the mutation operation to generate the trial vector and second, it selects the

best between the parent and the offspring based on their fitness value for the next

generation. If fitness of the parent is greater than the offspring then parent is

selected otherwise offspring is selected:

xi(G+ 1) =

⎧⎨⎩x′i(G), if f(x′i(G)) > f(xi(G)).

xi(G), otherwise.(4.7)

This ensures that the population’s average fitness does not deteriorate. The

Pseudo-code for DE method is described in Algorithm 4.3 [115].

Algorithm 4.3 Differential Evolutionary AlgorithmLet F and CR are the control parameters termed as scale factor and crossover probability respectively.Let P is the population vector.Initialize the control parameters F and CR;Create and initialize the population P (0) of NP individuals;while termination condition do

for each individual xi(G) ∈ P (G) doEvaluate the fitness f(xi(G));Create the trial vector ui(G) by applying the mutation operator;Create an offspring x′i(G) by applying the crossover operator;if f(x′i(G)) is better than f(xi(G)) then

Add x′i(G) to P (G+ 1);else

Add xi(G) to P (G+ 1);end if

end forend whileReturn the fittest individual as the solution.

63

4.4 Proposed Method



in terms of quality and robustness. It implants the color watermark image into

the color host image by modifying the decomposed wavelet coefficients of host

image. The each channel of color image is embedded in the corresponding channel

of host image to increase the reliability during the recovery process and protect

against the common signal processing attacks. The proposed method consists

of five phases namely; pre-processing of host and watermark image, watermark

embedding, image post-processing, extraction of watermark, and performance im-

provement using GA, ABC, and DE. The basic structural design of the proposed

method is shown in Figure 4.1. Since each color image has three channels, the

structural design and methodology represented in Figure 4.1 are repeated for all

the three channels. The details of each phase of the proposed method is described

in the following sections.


In image pre-processing, both the host RGB color image (H) and watermark image

(W ) are transformed into UCS color space using Eq. (2.8) which produces six

independent image components (three for host and three for watermark) namely;

HU , HC , and HS for host image and WU , WC , and WS for watermark image.

After transformation, each component of watermark image is divided into 16 non-

overlapping sub-areas as shown in Figure 4.2. These sub-areas of watermark image

are then re-arranged or scrambled by some pre-defined sequence or key to introduce

one more level of security and enforced the user to use the key for the extraction

of the image from watermarked image.

Further, third level of decomposition is applied on each component of host im-

age using symlet-4 wavelet function. The resultant third level wavelet coefficients,

(Hk3, k = {LL,HL, LH,HH}) is chosen for embedding process.

Watermark Embedding

After pre-processing of the images, the scrambled watermark is embedded into

the DWT coefficients of host image. The third level decomposition coefficients of

host image are HLL3(x, y), HHL3(x, y), HLH3(x, y), and HHH3(x, y) representing

the approximation, horizontal, vertical, and diagonal details of 3-DWT decom-

posed host image respectively. In order to increase the reliability and robustness

64


Figure 4.2: Watermark division.

65

of proposed method against the malicious attacks, it is desired to hide each color

component of the watermark image in corresponding component of host coeffi-

cients. Therefore, approximation details of the third level coefficients are divided

into 16 non-overlapping areas as depicted in Figure 4.3.

Figure 4.3: Partitioning of 3-DWT coefficients.

Now, the scrambled watermark is embedded into approximation details of the

third level decomposed host image coefficients by using Eq. (4.8).


k = {LL,HL, LH,HH}p = {1, 2, 3, ..., 16}r = {1, 2, 3, ..., 16}

(4.8)

here, k represents the sub-areas of host image, p is the intended block of water-


Post-processing

After embedding all the color channels of UCS watermark image into correspond-

ing DWT host image coefficients, inverse DWT (IDWT) is taken for watermarked

host image coefficients which returns watermarked image in UCS color space. Fi-

nally, UCS watermarked image is reconverted into RGB color space.


The watermark extraction process requires watermarked image and pre-defined

key. To extract the watermark, RGB watermarked image is transformed into UCS

color space and then 3-DWT decomposition using symlet-4 wavelet filter bank on

66

each channel is applied. To gather the fraction watermarked image, a reverse

process of embedding ia applied as shown in Figure 4.1 and using Eq. (4.9).

Finally, the scrambled watermark image is rearranged to its original sequence.


(x,y)

α(r)

k = {LL,HL, LH,HH}p = {1, 2, 3, ..., 16}r = {1, 2, 3, ..., 16}

(4.9)

Performance Improvement using Optimization Methods

The watermarked image HWk3 is subjected to various watermarking attacks which

degrade the performance of watermarking methods. The design of the apposite

attacks are as important as to design a method for protection of multimedia con-

tents because they require to test the robustness and security of the newly design

methods for protection of multimedia contents. There are mainly two parame-

ters namely quality and robustness which must be maximized for a watermarking




Therefore, this method uses three optimization methods namely; GA, ABC, and

DE for selecting the values of α which optimizes the quality and robustness in

terms of fitness function. The results of all the considered optimization methods

have been compared and analyzed.

There are 16 strength factors used in this chapter which become the dimension

of each individual in ABC/DE/GA method having the population size 50. The

optimized values of these 16 strength factors are calculated by minimizing the

following fitness function which is the modified version of Vahedi et al. [42],

Fitness =100

CPSNR+

100

SSIM+ 2×

90%∑Q=60%

[1−NCjpg(Q)]

+

50%∑Q=10%

[1−NCjpg(Q)] +

5∑i=1

[1−NCfilter(i)]

+3∑i=1

[1−NCscale(i)] +3∑i=1

[1−NCnoise(i)]

+

4∑i=1


(4.10)

67

here, CPSNR is composite peak signal-to-noise ratio, SSIM is structural similarity,

and NC is is normalized correlation calculated for the attacks namely; JPEG com-

pression (10 to 90%), filtering (wiener, mean, median), scaling (0.6 to 1.2 factor),

noise addition (poisson, salt and pepper, gaussian), rotation (10), and cropping

(10 to 40 %). These values are calculated by applying the mentioned attacks on

watermarked image. The setting of the parameters of three optimization methods

depicts in Table 4.1 for this experiment are given:

Table 4.1: Setting of the parameters of three optimization methods namely; GA,ABC, and DE.

GA ABC DE

Population size = 50. Colony size NP = 50. Population size NP = 50.

Crossover rate = 0.6. φij = rand[−1, 1]. The scale factor which controlsthe implication of the differentialvariation F = 0.5.

Crossover type = typically twopoint.

Number of food sources SN =NP/2.

The crossover probability CR =0.9.

Number of generations = 1000. limit = 1000. limit = 1000.

Mutation types = bit flip. The stopping criteria is maxi-mum number of function evalu-ations (which is set to be 500) isreached.

The stopping criteria is set to themaximum number of iterationswhich is 500 for this experiment.

4.5 Method Validation

The results obtained from the proposed method are compared with the methods

of Chou and Wu [96] and Su et al. [98], who also embedded the color watermark

images into the color host images. To compare these methods, quality and robust-

ness parameters are considered. The quality of watermarked image can be assess

by two ways namely; subjective and objective tests. Subjective test is done by

10 humans beings on the scale of 0 (very poor) to 5 (excellent) while objective

test calculates the parameters namely; CPSNR and SSIM. CPSNR measures the

degree of similarity between the original and watermarked image and is calculated

as follows [42]:

CPSNR =10

3

∑s

logM ×N × 2552∑M

m=1

∑Nn=1[H(m,n, s)−HW (m,n, s)]2

(4.11)

SSIM also measures the degree of similarity by including the three aspects of HVS

namely; loss of correlation (c), luminance distortion (l), and contrast distortion

68

(s). It is formulated as follows [98]:

SSIM = l(H,HW )c(H,HW )s(H,HW ) (4.12)

where,

l(H,HW ) =2μHμHW

+ C1

μ2H + μ2

HW+ C1

(4.13)

c(H,HW ) =2σHσHW

+ C2

σ2H + σ2

HW+ C2

(4.14)

s(H,HW ) =σHHW

+ C3

σHσHW+ C3

(4.15)

here μ and σ are mean and standard deviation respectively while C1, C2, and

C3 are three positive constants used to avoid a null denominator. The values of

CPSNR and SSIM must be maximized for effective quality of watermarked image.

The robustness of watermarking method shows the degree of similarity between

the original watermark image and extracted watermark image after applying the

attacks and is measured by normalized correlation (NC) which is formulated as

shown in Eq.4.16:

NC =1

3

∑s

∑Lm=1

∑Ln=1[W (m,n, s)× W (m,n, s)]∑Lm=1

∑Ln=1W

2(m,n, s)(4.16)

The values of NC lie in the rage of 0 (no similarity) to 1 (similar) and are calculated

for the extracted watermarks without attack and with attacks.


To compare the performance of proposed and considered color image watermarking

methods, four popularly used 24-bit RGB color images namely; lena, mandrill,

pepper, and sailboat, each of size 512 × 512, are used as host images which are

shown in Figure 4.4. In this chapter, one RGB color logo (64× 64) and one RGB

color image (64 × 64) namely; RTU logo and aeroplane color image respectively,

are used as watermark images which are shown in Figure 4.5. All the considered



69

(a) Lena (b) Mandrill (c) Pepper (d) Sailboat

Figure 4.4: RGB host images.

(a) RTU logo (b) Aeroplane

Figure 4.5: RGB watermark images.

In the pre-processing step of the proposed method, each host and watermark

images are converted into UCS color space followed by third level decomposition of

host images and scrambling of watermark images. Figure 4.6 shows the converted

UCS images of host and watermark and third level decomposed lena host image

is represented in Figure 4.7. The watermark image goes through the process of

scrambling. One of the possible scrambled watermark images of RTU logo and

aeroplane are shown in Figure 4.8.

Figure 4.7: Three level decomposed host lena image using DWT.

70

(a) (b) (c)

(d) (e) (f)

Figure 4.6: UCS host and watermark images (a). Lena, (b). Mandrill, (c).Pepper, (d). Sailboat, (e). RTU Logo, and (f). Aeroplane.


Figure 4.8: Representative scrambled UCS watermark images.

Now, the watermark images are embedded into each of the considered host

images and the resultant watermarked images are shown in Figure 4.9.

To compare the objective test for quality measures of resultant watermarked

images using proposed and considered methods, CPSNR and SSIM performance

parameters are calculated and shown in Table 4.2. From Table 4.2, it is validated

that all the methods including proposed method maintain the quality of the wa-

termarked image in terms of CPSNR (> 35dB) and SSIM (> 0.96). However,

the proposed method shows lower values of CPSNR and SSIM as compared to

other considered methods due to the hiding of complete watermark into all three

channels of host image while existing methods hide in only one channel of host

image. Moreover, subjective test on the resultant watermarked images is per-

formed and presented in Table 4.3. The results of subjective test show that the

proposed method effectively embeds the watermarks which is imperceptible by

human beings.

71

(a) (b) (c) (d)

(e) (f) (g) (h)

Figure 4.9: RGB watermarked images embedded by using DE-based proposedmethod (a)-(d) RTU logo and (e)-(h) Aeroplane image

Table 4.2: Comparison of CPSNR and SSIM values of watermarked imagesresultant from proposed and considered methods.

S.No. QualityParameters

Watermarkedimages

Su et al.

[98]Chouand Wu[96]

ProposedMethodusingGA

ProposedMethodusingABC

ProposedMethodusingDE

1. CPSNR Lena 36.57 37.79 35.81 35.92 36.01

Mandrill 36.42 37.71 35.13 35.67 35.89

Pepper 36.61 37.01 35.07 35.23 35.41

Sailboat 36.52 37.32 35.00 35.06 35.21

2. SSIM Lena 0.98 0.98 0.98 0.98 0.98

Mandrill 0.98 0.98 0.97 0.96 0.97

Pepper 0.96 0.97 0.96 0.96 0.96

Sailboat 0.98 0.97 0.97 0.97 0.97

Table 4.3: Average subjective quality comparison of original and watermarkedimages by 10 human beings in the scale of 0 to 5.

Average Score of 10 Human beings

S.No. Watermarkimage

Watermarkedimage

Su et al.

[98]Chouand Wu[96]

ProposedMethodusingGA

ProposedMethodusingABC

ProposedMethodusingDE

1. RTU Logo Lena 5 5 5 5 5

Mandrill 5 5 5 5 5

Pepper 5 5 5 5 5

Sailboat 5 5 5 5 5

2. Aeroplane Lena 5 5 5 5 5

Mandrill 5 5 5 5 5

Pepper 5 5 5 5 5

Sailboat 5 5 5 5 5

72

To show the effectiveness of the proposed watermarking method, the extracted

watermarks from the watermarked images and their NC values are shown in Figure

4.10 for each of the considered method. From Figure 4.10, it is visualized that the

proposed method and Su et al. [98] have highest NC values (1.0) for all extracted

watermarks and hence outperforms the method of Chou and Wu [96]. The columns

of Figure 4.10 show extracted watermarks from considered watermarked images

and first five rows show the extraction of RTU logo while last five show extraction

of aeroplane image using the proposed and considered methods of watermarking.


attacks on the watermarked images. In this chapter, attacks have been catego-

rized into two classes namely; common signal processing attacks and geometric

attacks. The considered common signal processing attacks consists of filtering at-

tacks (mean, median, wiener), noise attacks (gaussian, poisson, salt and pepper),

and JPEG compression attacks while rotation, scaling, and cropping are the con-

sidered geometric attacks. The attacks are applied on all the eight watermarked

images embedded with RTU logo and aeroplane images. The comparison of NC

values after applying the common signal processing attacks on watermarked im-

ages embedded with RTU logo are depicted in Table 4.4 while Table 4.5 shows

the NC values for watermarked images embedded with aeroplane image. After

applying the geometric attacks, the measured NC values for both the watermark

images are compared in Table 4.6. From Tables 4.4, 4.5, and 4.6, it is observed

that the robustness of watermarked images embedded with RTU logo have higher

values of NC as compared to aeroplane image due to the coarseness of aeroplane

image. The comparative results show that the proposed method using DE out-

performs other methods for all the considered attacks except JPEG compression

and rotation where the method of Su et al. shows slightly better robustness. The

similar performance of the proposed method can be observed from Figure 4.11 and

Figure 4.12 where the extracted RTU logo and Aeroplane watermarks by proposed

method using DE respectively, for all the considered attacks have been depicted.

Therefore, it is validated from the results that the proposed method using DE

produces high quality and better robust watermarked images and can be utilized

for content authentication to protect the copyrighted images.


This chapter proposes a novel DWT-based color image watermarking method us-

ing UCS. The use of uncorrelated color space increases the effective utilization of

all color channels of host image which is not feasible in correlated color spaces.

73

Used Watermarked Image

Method a. Lena b. Mandrill c. Pepper d. Sailboat

Chou and Wu [96]

(NC) 1.00 0.99 0.99 0.98

Su et al. [98]

(NC) 1.00 1.00 1.00 1.00

Proposed Method using GA

(NC) 1.00 1.00 1.00 1.00

Proposed Method using ABC

(NC) 1.00 1.00 1.00 1.00

Proposed Method using DE

(NC) 1.00 1.00 1.00 1.00

Chou and Wu [96]

(NC) 0.99 0.98 0.99 0.99

Su et al. [98]

(NC) 1.00 1.00 1.00 1.00

Proposed Method using GA

(NC) 1.00 1.00 1.00 1.00

Proposed Method using ABC

(NC) 1.00 1.00 1.00 1.00

Proposed Method using DE

(NC) 1.00 1.00 1.00 1.00

Figure 4.10: Comparison of extracted watermarks by considered and proposedmethods along with their corresponding NC values. Columns shows extractedwatermarks from watermarked image namely (a). Lena, (b). Mandrill, (c).

Pepper, and (d). Sailboat using the considered and proposed methods mentionedin first column. First five rows shows the extraction of RTU logo while last five

shows extraction of aeroplane watermark image.74

Tab

le4.4:

Com

parison

ofrobustnessin

term

sof

NC

values

obtained

afterap

plyingcommon

sign

alprocessingattackson

the

watermarkedim

ageem

bedded

withRTU

logo

S.N

o.

Attacks

lena

Mandril

Pepper

Sailboat

Chou

and

Wu

[96]

Su

et

al.

[98]

Propose

dM

eth

od

Chou

and

Wu

[96]

Su

et

al.

[98]

Propose

dM

eth

od

Chou

and

Wu

[96]

Su

et

al.

[98]

Propose

dM

eth

od

Chou

and

Wu

[96]

Su

et

al.

[98]

Propose

dM

eth

od

Using

Using

Using

Using

GA

ABC

DE

GA

ABC

DE

GA

ABC

DE

GA

ABC

DE

1Med

ian

filtering

(3×

3)

0.18

0.73

0.72

0.74

0.75

0.17

0.68

0.67

0.69

0.70

0.18

0.72

0.71

0.73

0.74

0.17

0.70

0.69

0.71

0.72

2Med

ian

filtering

(5×

5)

0.11

0.63

0.62

0.64

0.65

0.10

0.59

0.58

0.60

0.60

0.11

0.62

0.61

0.63

0.64

0.11

0.60

0.60

0.62

0.62

3W

iener

filtering

(3×

3)

0.67

0.87

0.86

0.89

0.90

0.62

0.81

0.80

0.83

0.83

0.66

0.85

0.84

0.87

0.88

0.64

0.84

0.83

0.85

0.86

4Mean

filtering(3

×

3)

0.54

0.96

0.95

0.98

0.99

0.50

0.89

0.88

0.91

0.92

0.53

0.94

0.93

0.96

0.97

0.52

0.92

0.91

0.94

0.95

5Mean

filtering(5

×

5)

0.41

0.87

0.86

0.89

0.90

0.38

0.81

0.80

0.83

0.83

0.40

0.85

0.84

0.87

0.88

0.39

0.84

0.83

0.85

0.86

6Gaussian

noise

(0.006)

0.90

0.82

0.81

0.84

0.84

0.84

0.76

0.75

0.78

0.79

0.88

0.80

0.80

0.82

0.83

0.86

0.79

0.78

0.80

0.81

7Poissonnoise

0.86

0.83

0.82

0.85

0.86

0.80

0.77

0.76

0.79

0.80

0.84

0.81

0.81

0.83

0.84

0.83

0.80

0.79

0.81

0.82

8Salt

and

pep

per

noise

0.90

0.91

0.89

0.93

0.94

0.84

0.85

0.84

0.86

0.87

0.88

0.89

0.88

0.91

0.92

0.86

0.87

0.86

0.89

0.90

9JPEG

compression

(10%)

0.14

0.71

0.70

0.69

0.70

0.13

0.66

0.65

0.64

0.65

0.14

0.70

0.69

0.67

0.68

0.13

0.68

0.67

0.66

0.67

10

JPEG

compression

(30%)

0.21

0.88

0.87

0.85

0.86

0.20

0.82

0.81

0.79

0.80

0.21

0.86

0.85

0.84

0.84

0.20

0.84

0.84

0.82

0.83

11

JPEG

compression

(60%)

0.34

0.90

0.83

0.87

0.88

0.32

0.84

0.83

0.81

0.82

0.33

0.88

0.87

0.86

0.86

0.33

0.86

0.86

0.84

0.85

12

JPEG

compression

(90%)

0.44

0.93

0.92

0.90

0.91

0.41

0.86

0.86

0.84

0.85

0.43

0.91

0.90

0.88

0.89

0.42

0.89

0.88

0.87

0.87

75

Tab

le4.5:

Com

parison

ofrobustnessin

term

sof

NC

values

obtained

afterap

plyingcommon

sign


the

watermarkedim

ageem

bedded

withAerop

laneim

age.

S.N

o.

Attacks

lena

Mandril

Pepper

Sailboat

Chou

and

Wu

[96]

Su

et

al.

[98]

Propose

dM

eth

od

Chou

and

Wu

[96]

Su

et

al.

[98]

Propose

dM

eth

od

Chou

and

Wu

[96]

Su

et

al.

[98]

Propose

dM

eth

od

Chou

and

Wu

[96]

Su

et

al.

[98]

Propose

dM

eth

od

Using

Using

Using

Using

GA

ABC

DE

GA

ABC

DE

GA

ABC

DE

GA

ABC

DE

1Med

ian

filtering

(3×

3)

0.13

0.71

0.70

0.72

0.73

0.12

0.66

0.65

0.67

0.68

0.13

0.70

0.69

0.71

0.72

0.12

0.68

0.67

0.70

0.70

2Med

ian

filtering

(5×

5)

0.06

0.62

0.61

0.63

0.64

0.06

0.58

0.57

0.59

0.59

0.06

0.61

0.60

0.62

0.63

0.06

0.60

0.59

0.61

0.61

3W

iener

filtering

(3×

3)

0.51

0.83

0.82

0.85

0.86

0.47

0.77

0.76

0.79

0.80

0.50

0.81

0.81

0.83

0.84

0.49

0.80

0.79

0.81

0.82

4Mean

filtering(3

×

3)

0.42

0.95

0.94

0.97

0.98

0.39

0.88

0.87

0.90

0.91

0.41

0.93

0.92

0.95

0.96

0.40

0.91

0.90

0.93

0.94

5Mean

filtering(5

×

5)

0.3

0.87

0.86

0.89

0.90

0.28

0.81

0.80

0.83

0.83

0.29

0.85

0.84

0.87

0.88

0.29

0.84

0.83

0.85

0.86

6Gaussian

noise

(0.006)

0.81

0.83

0.82

0.85

0.86

0.75

0.77

0.76

0.79

0.80

0.79

0.81

0.81

0.83

0.84

0.78

0.80

0.79

0.81

0.82

7Poissonnoise

0.7

0.81

0.80

0.83

0.83

0.65

0.75

0.75

0.77

0.78

0.69

0.79

0.79

0.81

0.82

0.67

0.78

0.77

0.79

0.80

8Salt

and

pep

per

noise

0.82

0.84

0.83

0.86

0.87

0.76

0.78

0.77

0.80

0.80

0.80

0.82

0.81

0.84

0.85

0.79

0.81

0.80

0.82

0.83

9JPEG

compression

(10%)

0.11

0.69

0.68

0.67

0.68

0.10

0.64

0.64

0.62

0.63

0.11

0.68

0.67

0.66

0.66

0.11

0.66

0.66

0.64

0.65

10

JPEG

compression

(30%)

0.16

0.86

0.85

0.83

0.84

0.15

0.80

0.79

0.78

0.78

0.16

0.84

0.83

0.82

0.83

0.15

0.83

0.82

0.80

0.81

11

JPEG

compression

(60%)

0.27

0.89

0.88

0.86

0.87

0.25

0.83

0.82

0.80

0.81

0.26

0.87

0.86

0.85

0.85

0.26

0.85

0.85

0.83

0.84

12

JPEG

compression

(90%)

0.4

0.91

0.90

0.88

0.89

0.37

0.85

0.84

0.82

0.83

0.39

0.89

0.88

0.87

0.87

0.38

0.87

0.86

0.85

0.86

76

Tab

le4.6:

Com

parison

ofrobustnessin

term

sof

NC

values

obtained

afterap

plyinggeom

etricattackson

thewatermarkedim

ages.

S.N

o.

Attacks

lena

Mandril

Pepper

Sailboat

Chou

and

Wu

[96]

Su

et

al.

[98]

Propose

dM

eth

od

Chou

and

Wu

[96]

Su

et

al.

[98]

Propose

dM

eth

od

Chou

and

Wu

[96]

Su

et

al.

[98]

Propose

dM

eth

od

Chou

and

Wu

[96]

Su

et

al.

[98]

Propose

dM

eth

od

Using

Using

Using

Using

GA

ABC

DE

GA

ABC

DE

GA

ABC

DE

GA

ABC

DE

ForRTU

Logo

Wate

rm

ark

Image

1Rotation(1

0)

0.86

0.93

0.92

0.91

0.92

0.80

0.86

0.86

0.85

0.86

0.84

0.91

0.90

0.90

0.91

0.83

0.89

0.88

0.87

0.88

2Scaling(0.6)

0.54

0.95

0.94

0.97

0.98

0.50

0.88

0.87

0.90

0.91

0.53

0.93

0.92

0.95

0.96

0.52

0.91

0.90

0.93

0.94

3Scaling(0.9)

0.63

0.97

0.96

0.99

1.00

0.59

0.90

0.89

0.92

0.93

0.62

0.95

0.94

0.97

0.98

0.60

0.93

0.92

0.95

0.96

4Scaling(1.2)

0.72

0.99

0.98

0.99

1.00

0.67

0.92

0.91

0.99

1.00

0.71

0.97

0.96

0.99

1.00

0.69

0.95

0.94

0.99

1.00

5Cropping(10%)

0.91

0.93

0.92

0.95

0.96

0.85

0.86

0.86

0.88

0.89

0.89

0.91

0.90

0.93

0.94

0.87

0.89

0.88

0.91

0.92

6Cropping(20%)

0.81

0.82

0.81

0.84

0.84

0.75

0.76

0.75

0.78

0.79

0.79

0.80

0.80

0.82

0.83

0.78

0.79

0.78

0.80

0.81

7Cropping(30%)

0.70

0.72

0.71

0.73

0.74

0.65

0.67

0.66

0.68

0.69

0.69

0.71

0.70

0.72

0.73

0.67

0.69

0.68

0.71

0.71

8Cropping(40%)

0.55

0.60

0.59

0.61

0.62

0.51

0.56

0.55

0.57

0.57

0.54

0.59

0.58

0.60

0.61

0.53

0.58

0.57

0.59

0.59

ForAeroplaneW

ate

rm

ark

Image

1Rotation(1

0)

0.73

0.9

0.89

0.88

0.89

0.68

0.84

0.83

0.82

0.83

0.72

0.88

0.87

0.85

0.86

0.70

0.86

0.86

0.83

0.84

2Scaling(0.6)

0.5

0.94

0.93

0.96

0.97

0.47

0.87

0.87

0.89

0.90

0.49

0.92

0.91

0.94

0.95

0.48

0.90

0.89

0.92

0.93

3Scaling(0.9)

0.59

0.97

0.96

0.99

1.00

0.55

0.90

0.89

0.92

0.93

0.58

0.95

0.94

0.97

0.98

0.57

0.93

0.92

0.95

0.96

4Scaling(1.2)

0.67

0.99

0.98

0.99

1.00

0.62

0.92

0.91

0.99

1.00

0.66

0.97

0.96

0.99

1.00

0.64

0.95

0.94

0.99

1.00

5Cropping(10%)

0.86

0.91

0.90

0.93

0.94

0.80

0.85

0.84

0.86

0.87

0.84

0.89

0.88

0.91

0.92

0.83

0.87

0.86

0.89

0.90

6Cropping(20%)

0.76

0.79

0.78

0.81

0.81

0.71

0.73

0.73

0.75

0.76

0.74

0.77

0.77

0.79

0.80

0.73

0.76

0.75

0.77

0.78

7Cropping(30%)

0.62

0.67

0.66

0.68

0.69

0.58

0.62

0.62

0.64

0.64

0.61

0.66

0.65

0.67

0.68

0.60

0.64

0.64

0.66

0.66

8Cropping(40%)

0.43

0.61

0.60

0.62

0.63

0.40

0.57

0.56

0.58

0.58

0.42

0.60

0.59

0.61

0.62

0.41

0.59

0.58

0.60

0.60

77


Median (3x3) Median (5x5) Wiener (3x3) Mean (3x3)

Mean (5x5) Gaussian (0.006) Poisson Salt & Pepper

JPEG (10%) JPEG (30%) JPEG (60%) JPEG (90%)



Figure 4.11: Extracted RTU logo watermarks by proposed method using DEafter applying considered attacks.

78







Figure 4.12: Extracted Aeroplane watermarks by proposed method using DEafter applying considered attacks.

79

Moreover, different optimization methods are used for optimizing the strength

factors to improve the quality and robustness of the proposed method. The per-

formance of the proposed methods have been measured in terms of quality and

robustness against different signal processing attacks and results are compared

with the work of Chou and Wu [96] and Su et al. [98]. The results validate that

the proposed method using DE is better than other methods for all the considered

parameters except slight decay in JPEG compression and rotation attacks as com-

pared to Su et al. [98]. Therefore, it is concluded that the proposed method using

DE has high quality and robust results and can further be used for protection of

the copyrighted images.

The next chapter introduces SPT-based watermarking method for color wa-

termark images.

80

Chapter 5

Digital Image Watermarking using Steer-

able Pyramid Transform on Color Water-

mark Images

5.1 Introduction

Previous chapter presented the DWT-based image watermarking method for pro-

tecting the color images against illegal uses by embedding the color watermark.

Further, optimization methods have been used to improve the performance. In

Chapter 3, the SPT-based image watermarking method has been proposed for

protecting the color images against illegal uses by embedding the gray-scale wa-

termark. Moreover, analysis of the results show that the SPT-based method out-

performs as compared to DWT. Therefore, this chapter proposes a method for

the protection of color images using SPT by embedding color watermark images.

Since, from the results of previous chapter, it is validated that DE outperforms

other optimization function, hence in this chapter, the DE has been used to opti-

mized the strength factors.

Rest of the chapter is organized as follows. Section 5.2 describes the proposed

method. The method validation parameters and experimental results are discussed

in Section 5.3 and 5.4 respectively. Finally, Section 5.5 concludes the chapter.

5.2 Proposed Method



in terms of quality and robustness. It implants the color watermark image into

the color host image by modifying the decomposed SPT coefficients of host im-

age. Each channel of color image is embedded in the corresponding channel of

host image to increase the reliability during the recovery process and to protect

against the common signal processing attacks. The proposed method consists

of five phases namely; pre-processing of host and watermark image, watermark

embedding, image post-processing, extraction of watermark, and performance im-

provement using DE. The basic structural design of the proposed method is shown

in Figure 5.1. The details of each phase of the proposed method are described in

the following sections.


In image pre-processing, both the host RGB color image (H) and watermark

image (W ) are transformed into UCS color space using Eq. (2.8) which pro-

duces six independent image components (three for host and three for watermark)

namely; HU , HC , and HS for host image and WU , WC , and WS for watermark im-

age. After transformation, each component of watermark image is divided into 16

non-overlapping sub-areas as shown in Figure 5.2. These sub-areas of watermark

image are then re-arranged or scrambled by some pre-defined sequence or key to

introduce one more level of security and enforced the user to use the key for the

extraction of the image from watermarked image. The pre-defined sequence or

key has 16 values (1-16) which are generated randomly. The watermark image is

scrambled according to this randomly generated sequence. One of the scrambled

pattern is shown in Image Preprocessing block of Figure 5.1. The above generated

pattern is stored for extraction phase. Further, third level of steerable pyramid

transformation decomposition is applied on each component of host image. The

resultant coefficients (Hk3, k = {1, 2, 3, ..., 16}) is chosen for embedding process.

Watermark Embedding

After pre-processing of the images, the scrambled watermark is embedded into the

SPT coefficients of host image. In order to increase the reliability and robustness

of proposed method against the malicious attacks, it is desired to hide each color

component of the watermark image in corresponding component of host coeffi-

cients. Now, the scrambled watermark is embedded into approximation details of

the third level decomposed host image coefficients by using Eq. (5.1).


k = {1, 2, 3, ..., 16}p = {1, 2, 3, ..., 16}r = {1, 2, 3, ..., 16}

(5.1)

82


here, k represents the sub-areas of host image, p is the intended block of water-


Post-processing

After embedding all the color channels of UCS watermark image into correspond-

ing SPT host image coefficients, inverse SPT (ISPT) is taken for watermarked host

83

Figure 5.2: Watermark division.

image coefficients which returns watermarked image in UCS color space. Finally,

UCS watermarked image is reconverted into RGB color space.


The watermark extraction process requires watermarked image and pre-defined

key. To extract the watermark, RGB watermarked image is transformed into UCS

color space and then SPT decomposition on each channel is applied. To recover the

watermarked image, a reverse process of embedding is applied as shown in Figure

5.1 by using Eq. (5.2). Finally, the scrambled watermark image is rearranged to

its original sequence.


(x,y)

α(r)

k = {1, 2, 3, ..., 16}p = {1, 2, 3, ..., 16}r = {1, 2, 3, ..., 16}

(5.2)

Performance Improvement using DE

The watermarked image is subjected to various watermarking attacks which de-

grade the performance of watermarking methods. The design of the apposite at-

tacks are as important as to design a method for protection of multimedia contents

because they are required to test the robustness and security of the newly design

methods for protection of multimedia contents. There are mainly two parame-

ters namely; quality and robustness which must be maximized for a watermarking




Therefore, this chapter uses DE optimization method for selecting the values of α

which optimizes the quality and robustness in terms of fitness function.

84

This method uses following settings of DE parameters:

• The crossover probability CR = 0.9.

• The scale factor which controls the implication of the differential variation

F = 0.5.

• Population size NP = 50.

• The stopping criteria is set to the maximum number of iterations which is

500 for this experiment.

There are 16 strength factors used in this method which become the dimension

of each individual in DE method. The optimized values of these 16 strength factors

are calculated by minimizing the following fitness function :

Fitness =100

CPSNR+

100

SSIM+ 2×

90%∑Q=60%

[1−NCjpg(Q)]

+50%∑

Q=10%

[1−NCjpg(Q)] +5∑i=1

[1−NCfilter(i)]

+

3∑i=1

[1−NCscale(i)] +

3∑i=1

[1−NCnoise(i)]

+

4∑i=1


(5.3)

here, CPSNR is composite peak signal-to-noise ratio, SSIM is structural similarity,

and NC is normalized correlation calculated for the attacks namely; JPEG com-



(10 to 40 %). These values are calculated by applying the mentioned attacks on

watermarked image.

5.3 Method Validation

The results obtained from the proposed method are compared with the methods

of Chou and Wu [96] and Su et al. [98] who also embedded the color watermark

images into the color host images. To compare these methods, quality and ro-

bustness parameters are considered. The quality of watermarked image can be

assess by two ways namely subjective and objective tests. Subjective test is done

by 10 humans beings on the scale of 0 (very poor) to 5 (excellent) while objective

85

test calculates the parameters namely CPSNR and SSIM. CPSNR measures the

degree of similarity between the original and watermarked image and is calculated

as follows [42]:

CPSNR =10

3

∑s

logM ×N × 2552∑M

m=1

∑Nn=1[H(m,n, s)−HW (m,n, s)]2

(5.4)

SSIM also measures the degree of similarity by including the three aspects of HVS

namely loss of correlation (c), luminance distortion (l), and contrast distortion (s).

It is formulated as follows [98]:

SSIM = l(H,HW )c(H,HW )s(H,HW ) (5.5)

where,

l(H,HW ) =2μHμHW

+ C1

μ2H + μ2

HW+ C1

(5.6)

c(H,HW ) =2σHσHW

+ C2

σ2H + σ2

HW+ C2

(5.7)

s(H,HW ) =σHHW

+ C3

σHσHW+ C3

(5.8)

here μ and σ are mean and standard deviation respectively while C1, C2, and

C3 are three positive constants used to avoid a null denominator. The values of

CPSNR and SSIM must be maximized for effective quality of watermarked image.

The robustness of watermarking method shows the degree of similarity between

the original watermark image and extracted watermark image after applying the

attacks and is measured by NC which is formulated as shown in Eq.5.9:

NC =1

3

∑s

∑Lm=1

∑Ln=1[W (m,n, s)× W (m,n, s)]∑Lm=1

∑Ln=1W

2(m,n, s)(5.9)

The value of NC lies in the rage of 0 (no similarity) to 1 (similar) and is calculated

for the extracted watermark without attack and with attacks namely; JPEG com-



(10 to 40 %).

86


To compare the performance of proposed and considered color image watermarking

methods, four popularly used 24-bit RGB color images namely; lena, mandrill,

pepper, and sailboat each of size 512 × 512 are used as host images which are

shown in Figure 5.3. In this chapter, one 24-bit RGB color logo and one RGB

color image namely; RTU logo and aeroplane color image respectively each of size

64 × 64 are used as watermark images which are shown in Figure 5.4. All the

considered images have been taken from USC-SIPI image database [112] except

RTU logo which is taken from Rajasthan Technical University, Kota, India.

(a) Lena (b) Mandrill (c) Pepper (d) Sailboat

Figure 5.3: RGB host images.


Figure 5.4: RGB watermark images.

In the pre-processing step of the proposed method, each host and watermark

images are converted into UCS color space followed by SPT decomposition of host

images and scrambling of watermark images. Figure 5.5 shows the converted UCS

images of host and watermark. The watermark image goes through the process

of scrambling. One of the possible scrambled watermark images of RTU logo and

aeroplane are shown in Figure 5.6. Now, the watermark images are embedded

into each of the considered host images and the resultant watermarked images are

shown in Figure 5.7.

A comparison of fitness values before and after optimization for 30 runs has

been depicted in Figure 5.8 which shows that the use of DE enhances the fitness

function value.

87

(a) (b) (c)

(d) (e) (f)

Figure 5.5: UCS host and watermark images (a). Lena, (b). Mandrill, (c).Pepper, (d). Sailboat, (e). RTU Logo, and (f). Aeroplane.


Figure 5.6: Representative scrambled UCS watermark images.

The performance of the proposed method is compared with method proposed

in the chapter 4, in which DWT and DE have been used to increases the quality

and robustness of image watermarking method. To compare the objective test for

quality measures of resultant watermarked images using proposed and considered

methods, CPSNR and SSIM performance parameters are calculated and depicted

in Table 5.1. From Table 5.1 it is validated that all the methods including proposed

method maintain the quality of the watermarked image in terms of CPSNR (>

35dB) and SSIM (> 0.95). However, the proposed method shows lower values of

CPSNR and SSIM as compared to other considered methods due to the hiding

of complete watermark into all the three channels of host image, while existing

methods hide in only one channel of the host image. Further, the results using

SPT is more promising than DWT-based method. Moreover, subjective test on

the resultant watermarked images is performed and presented in Table 5.2. The

results of subjective test show that the proposed method effectively embeds the

88

(a) (b) (c) (d)

(e) (f) (g) (h)

Figure 5.7: RGB watermarked images embedded by (a)-(d) RTU logo and(e)-(h) Aeroplane image

Figure 5.8: A comparison of before and after optimization of fitness values for 30runs.

watermarks which is imperceptible by human beings.

To show the effectiveness of the proposed watermarking method, the extracted

watermarks from the watermarked images and their NC values are shown in Figure

5.9 for each of the considered method. From Figure 5.9, it is visualized that the

proposed method using UCS color space and Su et al. [98] have highest NC values

(1.0) for all extracted watermarks and hence outperforms the method of Chou and

Wu [96]. The columns of Figure 5.9 show extracted watermarks from considered

watermarked images and first four rows shows the extraction of RTU logo while

last four shows extraction of aeroplane image using the proposed and considered

89

Table 5.1: Comparison of CPSNR and SSIM values of watermarked imagesresultant from proposed and considered methods.

S.No. QualityParameters

Watermarkedimages

Su et al.

[98]Chou andWu [96]

DWT-basedProposedMethod

SPT-basedProposedMethod

1. CPSNR Lena 36.94 38.17 36.01 36.28

Mandrill 36.78 38.09 35.89 36.03

Pepper 36.98 37.38 35.41 35.58

Sailboat 36.89 37.69 35.21 35.41

2. SSIM Lena 0.98 0.97 0.98 0.98

Mandrill 0.97 0.96 0.97 0.96

Pepper 0.97 0.97 0.96 0.96

Sailboat 0.97 0.97 0.97 0.97

Table 5.2: Average subjective quality comparison of original and watermarkedimages by 10 human beings in the scale of 0 to 5.

Average Score of 10 Human beings

S.No. Watermarkimage

Watermarkedimage

Su et al.

[98]Chou andWu [96]

DWT-basedProposedMethod

SPT-basedProposedMethod

1. RTU Logo Lena 5 5 5 5

Mandrill 5 5 5 5

Pepper 5 5 5 5

Sailboat 5 5 5 5

2. Aeroplane Lena 5 5 5 5

Mandrill 5 5 5 5

Pepper 5 5 5 5

Sailboat 5 5 5 5

methods of watermarking.


attacks on the watermarked images. In this chapter, attacks have been catego-

rized into two classes namely; common signal processing attacks and geometric

attacks. The considered common signal processing attacks consists of filtering at-

tacks (mean, median, wiener), noise attacks (gaussian, poisson, salt and pepper),

and JPEG compression attacks while rotation, scaling, and cropping are the con-

sidered geometric attacks. The attacks are applied on all the eight watermarked

images embedded with RTU logo and aeroplane images. The comparison of NC

values after applying the common signal processing attacks on watermarked im-

ages embedded with RTU logo are depicted in Table 5.3 while Table 5.4 shows

the NC values for watermarked images embedded with aeroplane image by all

considered methods and proposed method. After applying the geometric attacks,

the measured NC values for both the watermark images are compared in Table

5.5. From Tables 5.3 – 5.5, it is observed that the robustness of watermarked

images embedded with RTU logo have higher values of NC as compared to aero-

90

Used Watermarked ImageMethod a. Lena b. Mandrill c. Pepper d. Sailboat

Chou and Wu [96]

(NC) 0.98 0.99 0.99 0.98

Su et al. [98]

(NC) 1.00 1.00 1.00 1.00

DWT-based Proposed Method

(NC) 1.00 0.99 1.00 1.00

SPT-based Proposed Method

(NC) 1.00 1.00 1.00 1.00

Chou and Wu [96]

(NC) 0.99 0.98 0.99 0.99

Su et al. [98]

(NC) 1.00 1.00 1.00 1.00

DWT-based Proposed Method

(NC) 1.00 0.99 1.00 0.99

SPT-based Proposed Method

(NC) 1.00 1.00 1.00 1.00

Figure 5.9: Comparison of extracted watermarks by considered and proposedmethods along with their corresponding NC values. Columns shows extractedwatermarks from watermarked image namely (a). Lena, (b). Mandrill, (c).

Pepper, and (d). Sailboat using the considered and proposed methods mentionedin first column. First four rows shows the extraction of RTU logo while last four

shows extraction of aeroplane watermark image.

91

Tab

le5.3:

Com

parison

ofrobustnessin

term

sof

NC

values

obtained

afterap

plyingcommon

sign


the

watermarkedim

ageem

bedded

withRTU

logo

lena

Mandrill

Pepper

Sailboat

S.N

o.

Attacks

Chou

and

Wu

[96]

Su

et

al.

[98]

DW

T-

base

dPro-

pose

dM

eth

od

SPT-

base

dPro-

pose

dM

eth

od

Chou

and

Wu

[96]

Su

et

al.

[98]

DW

T-

base

dPro-

pose

dM

eth

od

SPT-

base

dPro-

pose

dM

eth

od

Chou

and

Wu

[96]

Su

et

al.

[98]

DW

T-

base

dPro-

pose

dM

eth

od

SPT-

base

dPro-

pose

dM

eth

od

Chou

and

Wu

[96]

Su

et

al.

[98]

DW

T-

base

dPro-

pose

dM

eth

od

SPT-

base

dPro-

pose

dM

eth

od

1Med

ian

filtering

(3×

3)

0.18

0.74

0.75

0.76

0.17

0.68

0.70

0.70

0.18

0.72

0.74

0.74

0.17

0.71

0.72

0.73

2Med

ian

filtering

(5×

5)

0.11

0.63

0.65

0.65

0.10

0.59

0.60

0.61

0.11

0.62

0.64

0.64

0.11

0.61

0.62

0.63

3W

iener

filtering

(3×

3)

0.68

0.88

0.90

0.90

0.63

0.82

0.83

0.84

0.66

0.86

0.88

0.88

0.65

0.84

0.86

0.86

4Mean

filtering

(3×

3)

0.54

0.97

0.99

0.99

0.51

0.90

0.92

0.92

0.53

0.95

0.97

0.97

0.52

0.93

0.95

0.95

5Mean

filtering

(5×

5)

0.41

0.88

0.90

0.90

0.38

0.82

0.83

0.84

0.40

0.86

0.88

0.88

0.40

0.84

0.86

0.86

6Gaussian

noise

(0.006)

0.91

0.83

0.84

0.85

0.84

0.77

0.79

0.79

0.89

0.81

0.83

0.83

0.87

0.79

0.81

0.81

7Poissonnoise

0.87

0.84

0.86

0.86

0.81

0.78

0.80

0.80

0.85

0.82

0.84

0.84

0.83

0.80

0.82

0.82

8Salt

and

pep

per

noise

0.91

0.92

0.94

0.94

0.84

0.85

0.87

0.88

0.89

0.90

0.92

0.92

0.87

0.88

0.90

0.90

9JPEG

compres-

sion(10%)

0.14

0.72

0.70

0.70

0.13

0.67

0.65

0.65

0.14

0.70

0.68

0.69

0.14

0.69

0.67

0.67

10

JPEG

compres-

sion(30%)

0.21

0.89

0.86

0.87

0.20

0.82

0.80

0.81

0.21

0.87

0.84

0.85

0.20

0.85

0.83

0.83

11

JPEG

compres-

sion(60%)

0.34

0.91

0.88

0.89

0.32

0.84

0.82

0.82

0.34

0.89

0.86

0.87

0.33

0.87

0.85

0.85

12

JPEG

compres-

sion(90%)

0.44

0.94

0.91

0.92

0.41

0.87

0.85

0.85

0.43

0.92

0.89

0.90

0.43

0.90

0.87

0.88

92

Tab

le5.4:

Com

parison

ofrobustnessin

term

sof

NC

values

obtained

afterap

plyingcommon

sign


the

watermarkedim

ageem

bedded

withAerop

laneim

age.

lena

Mandrill

Pepper

Sailboat

S.N

o.

Attacks

Chou

and

Wu

[96]

Su

et

al.

[98]

DW

T-

base

dPro-

pose

dM

eth

od

SPT-

base

dPro-

pose

dM

eth

od

Chou

and

Wu

[96]

Su

et

al.

[98]

DW

T-

base

dPro-

pose

dM

eth

od

SPT-

base

dPro-

pose

dM

eth

od

Chou

and

Wu

[96]

Su

et

al.

[98]

DW

T-

base

dPro-

pose

dM

eth

od

SPT-

base

dPro-

pose

dM

eth

od

Chou

and

Wu

[96]

Su

et

al.

[98]

DW

T-

base

dPro-

pose

dM

eth

od

SPT-

base

dPro-

pose

dM

eth

od

1Med

ian

filtering

(3×

3)

0.13

0.72

0.73

0.74

0.12

0.67

0.68

0.68

0.13

0.70

0.72

0.72

0.13

0.69

0.70

0.71

2Med

ian

filtering

(5×

5)

0.06

0.63

0.64

0.64

0.06

0.58

0.59

0.60

0.06

0.61

0.63

0.63

0.06

0.60

0.61

0.62

3W

iener

filtering

(3×

3)

0.51

0.84

0.86

0.86

0.48

0.78

0.80

0.80

0.50

0.82

0.84

0.84

0.49

0.80

0.82

0.82

4Mean

filtering

(3×

3)

0.42

0.96

0.98

0.98

0.39

0.89

0.91

0.91

0.42

0.94

0.96

0.96

0.41

0.92

0.94

0.94

5Mean

filtering

(5×

5)

0.30

0.88

0.90

0.90

0.28

0.82

0.83

0.84

0.30

0.86

0.88

0.88

0.29

0.84

0.86

0.86

6Gaussian

noise

(0.006)

0.82

0.84

0.86

0.86

0.76

0.78

0.80

0.80

0.80

0.82

0.84

0.84

0.78

0.80

0.82

0.82

7Poissonnoise

0.71

0.82

0.83

0.84

0.66

0.76

0.78

0.78

0.69

0.80

0.82

0.82

0.68

0.78

0.80

0.81

8Salt

and

pep

per

noise

0.83

0.85

0.87

0.87

0.77

0.79

0.80

0.81

0.81

0.83

0.85

0.85

0.79

0.81

0.83

0.83

9JPEG

compres-

sion(10%)

0.11

0.70

0.68

0.68

0.10

0.65

0.63

0.63

0.11

0.68

0.66

0.67

0.11

0.67

0.65

0.65

10

JPEG

compres-

sion(30%)

0.16

0.87

0.84

0.85

0.15

0.81

0.78

0.79

0.16

0.85

0.83

0.83

0.15

0.83

0.81

0.81

11

JPEG

compres-

sion(60%)

0.27

0.90

0.87

0.88

0.25

0.83

0.81

0.81

0.27

0.88

0.85

0.86

0.26

0.86

0.84

0.84

12

JPEG

compres-

sion(90%)

0.40

0.92

0.89

0.90

0.38

0.85

0.83

0.83

0.40

0.90

0.87

0.88

0.39

0.88

0.86

0.86

93

Tab

le5.5:

Com

parison

ofrobustnessin

term

sof

NC

values

obtained

afterap

plyinggeom

etricattackson

thewatermarkedim

ages.

lena

Mandrill

Pepper

Sailboat

S.N

o.

Attacks

Chou

and

Wu

[96]

Su

et

al.

[98]

DW

T-

base

dPro-

pose

dM

eth

od

SPT-

base

dPro-

pose

dM

eth

od

Chou

and

Wu

[96]

Su

et

al.

[98]

DW

T-

base

dPro-

pose

dM

eth

od

Propose

dM

eth

od

using

UCS

Chou

and

Wu

[96]

Su

et

al.

[98]

DW

T-

base

dPro-

pose

dM

eth

od

SPT-

base

dPro-

pose

dM

eth

od

Chou

and

Wu

[96]

Su

et

al.

[98]

DW

T-

base

dPro-

pose

dM

eth

od

SPT-

base

dPro-

pose

dM

eth

od

ForRTU

Logo

Wate

rm

ark

Image

1Rotation(1

0)

0.87

0.94

0.92

0.95

0.81

0.87

0.86

0.88

0.85

0.92

0.91

0.93

0.83

0.90

0.88

0.90

2Scaling(0.6)

0.55

0.96

0.98

0.98

0.51

0.89

0.91

0.91

0.53

0.94

0.96

0.96

0.52

0.92

0.94

0.94

3Scaling(0.9)

0.64

0.98

1.00

0.98

0.59

0.91

0.93

0.93

0.62

0.96

0.98

0.98

0.61

0.94

0.96

0.96

4Scaling(1.2)

0.73

0.98

1.00

0.98

0.68

0.93

1.00

0.98

0.71

0.98

1.00

0.98

0.70

0.96

1.00

0.97

5Cropping(10%)

0.92

0.94

0.96

0.96

0.85

0.87

0.89

0.90

0.90

0.92

0.94

0.94

0.88

0.90

0.92

0.92

6Cropping(20%)

0.82

0.83

0.84

0.85

0.76

0.77

0.79

0.79

0.80

0.81

0.83

0.83

0.79

0.80

0.81

0.81

7Cropping(30%)

0.71

0.73

0.74

0.75

0.66

0.68

0.69

0.69

0.69

0.71

0.73

0.73

0.68

0.70

0.71

0.72

8Cropping(40%)

0.56

0.61

0.62

0.62

0.52

0.56

0.57

0.58

0.54

0.59

0.61

0.61

0.53

0.58

0.59

0.60

ForAeroplaneW

ate

rm

ark

Image

1Rotation(1

0)

0.74

0.91

0.89

0.91

0.69

0.85

0.83

0.86

0.72

0.89

0.86

0.89

0.71

0.87

0.84

0.86

2Scaling(0.6)

0.51

0.95

0.97

0.97

0.47

0.88

0.90

0.91

0.49

0.93

0.95

0.95

0.48

0.91

0.93

0.93

3Scaling(0.9)

0.60

0.98

1.00

0.98

0.55

0.91

0.93

0.93

0.58

0.96

0.98

0.98

0.57

0.94

0.96

0.96

4Scaling(1.2)

0.68

0.98

1.00

0.98

0.63

0.93

1.00

0.98

0.66

0.98

1.00

0.98

0.65

0.96

1.00

0.97

5Cropping(10%)

0.87

0.92

0.94

0.94

0.81

0.85

0.87

0.88

0.85

0.90

0.92

0.92

0.83

0.88

0.90

0.90

6Cropping(20%)

0.77

0.80

0.81

0.82

0.71

0.74

0.76

0.76

0.75

0.78

0.80

0.80

0.74

0.77

0.78

0.79

7Cropping(30%)

0.63

0.68

0.69

0.69

0.58

0.63

0.64

0.65

0.61

0.66

0.68

0.68

0.60

0.65

0.66

0.67

8Cropping(40%)

0.43

0.62

0.63

0.63

0.40

0.57

0.58

0.59

0.43

0.60

0.62

0.62

0.42

0.59

0.60

0.61

94

Extracted Watermark Image After Attacks






Figure 5.10: Extracted RTU logo watermarks by proposed method using UCSand DE after applying considered attacks.

95







Figure 5.11: Extracted Aeroplane watermarks by proposed method using UCSand DE after applying considered attacks.

96

plane image due to the coarseness of aeroplane image as compared to RTU logo.

The comparative results show that the proposed method using SPT, UCS color

space, and DE method outperforms other methods for all the considered attacks

except JPEG compression where the method of Su et al. [98] shows slightly better

robustness. The similar performance of the proposed method can be observed

from Figures 5.10 and 5.11 where the extracted RTU logo and aeroplane image

watermarks by proposed method using SPT, UCS, and DE for all the considered

attacks have been depicted. Therefore, it is validated from the results that the

proposed method using SPT, UCS color space, and DE produces high quality and

better robust watermarked images and can be utilized for content authentication

to protect the copyrighted images.


This chapter proposes a novel SPT-based color image watermarking method using

UCS and DE method. The use of uncorrelated color space increases the effective

utilization of all color channels of host image which is not feasible in correlated

color spaces while DE is used for optimizing the strength factors to improve the

quality and robustness of the proposed method. The performance of the proposed

method has been measured in terms of quality and robustness against different

signal processing attacks and results are compared with the work of Chou and

Wu [96] and Su et al. [98]. The results validate that the proposed method is

better than other methods for all the considered parameters except slight decay

in JPEG compression attack as compared to Su et al. [98]. Moreover, the results

are also compared with DWT-based method introduce in Chapter 4. The results

depict that SPT-based method outperforms DWT-based method. Therefore, it

is concluded that the proposed method using SPT, UCS color space, and DE

has high quality and robust results and can further be used for protection of the

copyrighted images.

97

Chapter 6

Conclusions and Scope for Future Work

In this thesis, an attempt has been made to identify the problems pertaining to

image watermarking for protection of color images. Color spaces, transform meth-

ods, optimization methods, and attacks are the major problems encountered in

the image watermarking area. In this work, some inherent drawbacks of exist-

ing methods used for quantitative analysis of watermarking for color images are

studied. The outcome of this study motivated to develop an image watermarking

system for the content authentication of color images.

Though the experimental results along with the discussions have been given

at the end of each chapter, this concluding chapter is mainly devoted to the con-

tributions made in this thesis. The main contributions in this thesis are four-fold.

First, pre-process the input color images (host and watermark) by transforming it

into UCS color space. Second, four efficient transform-based image watermarking

methods for color images have been proposed to enhance the transparency and

robustness of method. Third, three optimization methods have been used to im-

proves the performance of proposed methods by optimally selecting the strength

factors of watermarks and then post-processed the watermarked coefficients to re-

construct the watermarked image. Fourth, applying various watermarking attacks

in order to test the proposed method on various benchmark/ validation parameters

like composite-peak-signal-to-noise ratio (CPSNR), structural similarity (SSIM),

and normalized correlation (NC). Finally, an image watermarking method is de-

signed and developed using the proposed methods for protection of color images.

The thesis is concluded with a summary of contribution followed by some pointers

to new research that are directly related to the present work.

6.1 Contributions Made in the Thesis

The major contributions of this work are discussed below.

1. Digital Image Watermarking using Discrete Wavelet Transform on

Gray-Scale Watermark Image

The first contribution is to develop a robust image watermarking method

based on the DWT and UCS for the protection of color images by embedding

the gray-scale image watermarks. Further, the capabilities of GA have been

used to optimize 16 strength factors to improves the transparency and the

robustness of proposed method. In this method, the watermark images

are embeds in third level decomposed image by using symlet-4 filter bank.

Experimental results show that the proposed method has good quality and

robustness against the 16 considered signal processing and geometric attacks.

Moreover, the performance of the proposed method is better than the image

watermarking method by Vahedi et al. [42] who also used DWT and GA

method.

2. Digital Image Watermarking using Steerable Pyramid Transform

on Gray-Scale Watermark Images

The second contribution is devoted to design an image watermarking method

based on the SPT and UCS. In this method a gray-scale watermark has been

embedded in third level decompose coefficients of input image. Furthermore,

GA has been used to enhance the performance of the method. Experimen-

tal results show that the proposed method has good quality and robustness

against the 16 considered signal processing and geometric attacks. Moreover,

the performance of the proposed method is better than the image water-

marking method by Vahedi et al. [42] and the previously proposed method

(Digital Image Watermarking using Discrete Wavelet Transform on Gray-

Scale Watermark Image) which also used DWT and GA in their method.

Hence, it is concluded that the SPT outperforms the DWT.

3. Digital Image Watermarking using Discrete Wavelet Transform on

Color Watermark Images

The third contribution introduces a robust and blind image watermarking

method based on DWT and UCS for the protection of color images by em-

bedding the color image watermark into the third level decomposed host

coefficients. Moreover, to increase the reliability the watermark hides into

the multiple sections of the host image. Further, the capabilities of three

100

optimization methods namely; GA, ABC, and DE, have been exploited to op-

timize the 16 strength factors. Experimental results show that the proposed

method with DE has better quality and robustness against the 20 consid-

ered signal processing and geometric attacks. Moreover, the performance of

the proposed method is better than the image watermarking methods pro-

posed by Su et al. [98] and Chou and Wu [96] who also embedded the color

watermark.

4. Digital Image Watermarking using Steerable Pyramid Transform

on Color Watermark Images

The fourth contribution is based on the SPT-based image watermarking

method for the protection of color host images by embedding the color wa-

termark into the third level decomposed of image. Moreover, to increase

the reliability, the watermark is embedded into the multiple areas of the

host image. Further, the capabilities of the newly introduce DE method

has been exploited to optimize the 16 strength factors. Experimental results

show that the proposed method has better quality and robustness against

the 20 considered signal processing and geometric attacks. Moreover, the

performance of the proposed method is better than the image watermark-

ing methods proposed by Su et al. [98], Chou and Wu [96], and previously

proposed method (Chapter 4) who have also embedded the color watermark.

6.2 Scope for Future Work

The present work opens a lot of new avenues and directions for research in the

field of watermarking. Some of the possible directions for future research related

to the present work are given below;

• The developed methods may be explored for recently introduced transforms

and color spaces.

• The proposed work can be further explored by using hybrid methods like

more than one transform, color space, etc.

• The present work can be further investigated for other multimedia contents

such as video signals and 3-D models.

• The developed methods may be further explored for other newly introduced

optimization methods.

101

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List of Publications

1. M. Gupta, G. Parmar, R. Gupta, and M. Saraswat, “Discrete wavelet

transform-based color image watermarking using uncorrelated color space

and artificial bee colony”, International Journal of Computational Intelli-

gence Systems, Taylor & Francis, Vol. 8 (2), pp. 364-380, 2015.

[Published]

2. M. Gupta, G. Parmar, R. Gupta, and M. Saraswat, “Color image water-

marking using steerable pyramid transform and uncorrelated color space”,

Journal of Experimental and Theoretical Artificial Intelligence, Taylor &

Francis. [Under Review after Third Minor Revision]

3. M. Gupta, G. Parmar, R. Gupta, and M. Saraswat, “Digital image water-

marking using steerable pyramid transform and uncorrelated color space”,

in Proc. of Ninth International Conference on Industrial and Information

Systems (ICIIS 2014), India, 2014.

[Published]

4. M. Gupta, G. Parmar, R. Gupta, and M. Saraswat, “Digital image wa-

termarking using uncorrelated color space”, in Proc. of IEEE Symposium

on Computer Applications & Industrial Electronics (ISCAIE 2014), Penang,

Malaysia, 2014.

[Published]

5. M. Gupta, G. Parmar, R. Gupta, and R. Saraswat, “Authentication of

Multimedia Assets using Digital Watermarking: A Review”, in Proc. of

International Conference on Electronic Communication & Instrumentation

(e-Manthan 2012), India, 2012. [Published]

115

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