Copyright Protection for Digital Images Using Digital ... · Digital watermarking technique is a technique to allow a cover image to be embedded with a watermark image, for the protection

Copyright Protection for Digital Images

Using Digital Watermarking

By

Muhammad Asim

MCS07133009

MS Computer Science

The University of Lahore Sargodha Campus - Pakistan

Fall

2015

ii

The University of Lahore

Copyright Protection for Digital Images Using Digital Watermarking

A Thesis Presented to

The University of Lahore, Sargodha Campus

In partial fulfillment of the requirement for the degree of

MS Computer Science

By

Muhammd Asim

MCS07133009

Fall 2015

iii



A Post Graduate Thesis submitted to the Department of Computer Science as

partial fulfillment of the requirement for the award of Degree of M.S Computer

Science.

Name

Registration Number

Muhammad Asim

MCS07133009

Supervisor

Mr. Tahir Nawaz

Assistant Professor Department of Computer Science

The University of Lahore

Sargodha Campus.

January, 2016.

iv

Final Approval

This thesis titled



By

Muhammad Asim

MCS07133009

Has been approved

For The University of Lahore, Sargodha Campus

External Examiner:

Supervisor:

Mr. Tahir Nawaz

Assistant Professor Department of Computer Science

HoD:

Prof. Dr .Parvez khan Rabbani

HoD Department of Computer Science

v

Declaration

Muhammad Asim MCS07133009 hereby declare that I have produced the work

presented in this thesis, during the scheduled period of study. I also declare that I have

not taken any material from any source except referred to wherever due that amount of

plagiarism is within acceptable range. If a violation of HEC rules on research has

occurred in this thesis, I shall be liable to punishable action under the plagiarism rules

of the HEC.

Dated: Signature of Student

Muhammad Asim

MCS07133009

1-vi

Certificate

It is certified that Muhammad Asim MCS07133009 has carried out all the work related

to this thesis under my supervision at the Department of Computer Science, The

University of Lahore, Sargodha campus and the work fulfills the requirement for award of

MS degree.

Dated: Supervisor:

Mr. Tahir Nawaz

Assistant Professor

Head of Department:

Prof. Dr. Pervaiz khan Rabbani

HoD Department of Computer Science

vii

DEDICATION

I dedicate this work to my beloved parents, whose devotion for knowledge and

research has always been greatest source of inspiration behind my academic success.

viii

ACKNOWLEDGEMENTS

I most humbly pray to Allah Almighty for His unbounded bounties bestowed

upon me to acquire and disseminate it in the form of this thesis. Foremost, I

would like to express my sincere gratitude to my supervisor Mr.Tahir Nawaz,

who had been gracious enough to guide and supervise my research process

for MS thesis. His guidance helped me in all the time of research and writing

of this thesis. Besides my advisor, I would like to thank Prof. Dr.Pervaiz khan Rabbani ,

Professor of CS & IT department, for his guidance and

immense knowledge. I am also grateful to many people who directly and indirectly helped me

with the development and production of this thesis. Last but

not the least, I would like to thank my family: my parents, for their endless

love, prayers and their continuous support throughout my life.

ix

x



Abstract

Digital watermarking technique is the hidden information for the protection of copyright.

Digital watermarking technique is a technique to allow a cover image to be embedded with a

watermark image, for the protection and security purpose for the ownership or for image

hiding. In this study, a Singular Value Decomposition (SVD) based digital watermarking

technique using interpolation method is proposed where the hidden information is added in

the singular values of digital images using interpolation method. The desire watermarked

image is treated with different aspects of distortion operations. Finally, the watermark is

extracted from digital images for each method and each extracted watermark image is then

compared with the original watermark by using normalized correlation method, peak signal to

noise ratio and accuracy rate is also measured. A better scheme is also applied based on

comparison results. Finally, the better technique is implemented in real world application for

the purpose of copyright protection for digital images, authentication, integrity etc.

xi

TABLE OF CONTENTS

Table of Contents Type chapter title (level 1) .................................................................................................................... 1

Type chapter title (level 2) ................................................................................................................. 2

Type chapter title (level 3) ............................................................................................................. 3

Type chapter title (level 1) .................................................................................................................... 4

Type chapter title (level 2) ................................................................................................................. 5

Type chapter title (level 3) ............................................................................................................. 6

xii

List of Figures

xv

List of Tables

xvi

LIST OF ABBREVIATIONS

SVD Singular Value Decomposition

DIW Digital Image Watermark

NC Normalized Correlation

NCC Normalized Correlation co- ordination

CP Correct Pixel

NP No.of Pixel

AR Accuracy Rate

PSNR Peak Signal to Noise Ratio

DCT Discrete Cosine Transform

DWT Discrete Wavelet Transform

MSE Mean Squared Error

JAMA Java Matrix

1

Chapter 1

1. Introduction

4

The increasing to the access of digital images through internet and increasing the powerful

editing tool of digital images is an important issue nowadays for the owner’ [1]. A digital

watermark is defined as embedding the additional information into the host image for the

purpose of verification and authentication for the owner’s. Digital watermark can be visible

and invisible, a visible watermark can be typical a visible message on the image or on the

logos of the company and invisible watermark is hidden information in the image which is

for verification and authentication for the protection of copy right. Our work is on the digital

image copy right protection using Singular value decomposition method (SVD). This work

has already done but using different algorithms. Those algorithms have some issues i.e. time

complexity, difficult for embedding the watermark for developer etc. SVD is based on Linear

Algebra which is most efficient method. There are many types of watermarking, audio

watermark, text watermark, image watermark etc. In audio watermark, an additional audio

sound is added in existing audio materials [2]. Robust and transparent watermarking scheme

is also proposed for color images

1.1 Problem Statement

The new technologies enable us to process digital content with lesser time, lower complexities

and better efficiency. However, digitization also brings in disadvantages like illegal

reproduction and distribution of digital content. Nowadays, the spreading of digital

multimedia has made copyright protection a necessity. Too more issues like authentication

and information hiding have also become important issues.

To solve these kinds of authentication and copyright issues, various methods have been

developed to protect the digital content. This study will protect the copyright digital images

using digital watermarking. This technique should resist most of the modifications and

preserve the embedded information.

1.2 Objectives

Digital contents are rapidly increasing in the whole world day by day and protection is

necessary for multimedia contents. Multimedia included text, audio, video, and images should

be secure and protect from unauthorized user. A digital watermarking is used to protect the

multimedia contents. Digital watermark is embedded into the original image, text, and audio

5

or in video which is almost invisible. Watermark is a minor modification in original image

which is invisible and a secret information is embedded into the content. Our Aims and

Objectives is to protect copyright for digital images for the vendor’s.

1.3 Research Questions

This thesis will answers the following questions

1. The proposed technique will facilitate the digital images?

2. The digital watermarking technique is similar to as steganography?

3. The watermark technique will implemented for real life applications?

1.4 Scope To protect the

Copy right

1.5 Methodology

This study is based upon the updated literature review that identified digital watermarking in

multimedia for the protection of copyright. Digital watermarking algorithm is based on two

mathematical techniques. First is Singular value decomposition (SVD) and interpolation

method viz. Linear algebra and cosine interpolation method.

A cover image and watermark image is decomposed into respective SVD. After this, the

singular value of watermark image is found by using interpolation method. This study will be

implemented in matlab tool.

6

,

1.6 Thesis Layout

This thesis presents a detailed study of digital watermarking techniques and its literature

reviews, problem statement and its solution by the proposed watermark technique,

simulations and results and a brief comparison of results with the conventional hidden

information techniques and digital watermarking techniques.

• Chapter 1: Introduction

In this chapter, introduction to the problem associated with protection for the

digital images, research objectives and methodology are briefly described. The

research questions that need to be answered in this project are also discussed.

• Chapter 2: Literature Review

A lot of work has been done in the literature about digital watermarking

techniques. Those techniques which are found to be efficient and effective are

described in this chapter.

• Chapter 3:

In this chapter, background theory related to the project and technological review

have been presented.

• Chapter 4: Proposed Technique

In this chapter, the proposed digital watermark technique and its implementation

and model are presented.

• Chapter 5: Simulation and Results

In this chapter, the simulation of the proposed digital watermark technique has

been carried out and it has been compared with conventional hidden information

techniques.

7

• Chapter 6: Conclusion and Future work

In this chapter, the key findings of the research are described briefly in the

conclusion. Since there is always a room for improvements, therefore, the

suggestions and goals for further research based on this project are also discussed

in Future Work.

• References:

At the end of the thesis, references of research materials such as research papers,

thesis and international proceedings etc. which have been helpful in completion

of this project, are provided for detailed study.

8

Chapter 2

Literature Review

9

In early years, encryption and control access techniques were used to protect

ownership of media. But nowadays in digital world, watermark technique is

introduced to keep the copyright of multimedia. Digital watermark is to embedding

a special hidden pattern in multimedia using some algorithms. DCT-based and

wavelet-based technique are mostly used in watermark. Watermark can be measured

using similarity based and data correlation based methods [3].

2.1 Digital Image watermarking

“Digital watermarking” is a process of hiding digital information in a carrier signal

[4] [5]. Digital image watermarking embedding some information in cover image

before it is globally posted. Embedding watermarking in cover image show in below

figure.

Figure 1: Block diagram of watermarking process

The information to be embedded in the digital image is called a digital watermark.

The signal where the watermark is to be embedded is called the host signal.

The watermark process is divided into three distinct steps which are given below.

I. Embedding

II. Attack

III. Detection/extraction

Watermark is embedded in grayscale images which are used for valuable

applications. A model HVS (Human visual system) is trained using Fuzzy-BPN

hybrid technique to produced novel image watermark scheme to embed the

grayscale images [4].

10

Digital image watermarking techniques have been presented using fuzzy logic and

tabu search. In this approach image is divided into blocks and then parametric slant-

Hadamard transform is applied to each block individually. Then the watermark is

embedding in the transform domain and inverse transform is obtained. The selected

transform includes some parameters such as robustness and imperceptibility. The

robustness and imperceptibility are conflict to each other, however apply the

transform parameters to enhance the robustness by tabu search and the embedding

the watermark into image [5].

Software copy right using hiding watermarks in various data structures used by the

code B+-Trees, R-Trees, Linked lists. The main problem is that embedding of the

watermarking is should have minimum effect on the operations and performance of

data structures. There are two watermarking techniques. One is static watermarking

and second one is dynamic watermarking. In static watermarking the water is stored

in the source code, may be in data section or in the code section and the second

technique is dynamic watermarking stores the watermark in the program execution

static. Hiding watermark in data structures, which are used by the program, would

make the more robust [6].

The large datasets which are generated from very large databases are being mined

to take out secret knowledge and patterns which are useful for decision making. A

model has been developed for this special purpose, a framework model which

develop usability constraints for all types of datasets. The second one is novel

watermarking technique that facilitates for, non-commercial and strings datasets.

Watermarking scheme not only preserve the classification potential of features but

also results in approximately zero information loss. Two main phases in these

watermarking scheme one is watermark encoding and second one is watermark

decoding [8]. An audio watermarking method using ideas of wavelet-based entropy.

The method converts low-frequency coefficients of discrete wavelet transform

(DWT) into wavelet based entropy (WBE) domain. This process followed by the

calculations of mean values of each audio as well as derivation of some essential

properties of WBE, watermark can be applied on using only values of the WBE [9].

A novel blind watermark technique has been introduced based on the back

propagation and artificial neural network is proposed. The proposed watermarking

on HVS images in which take inputs with its weights and then applied a threshold

11

then trained the whole model and produced the output with embedding watermark.

This method is mostly applied on image processing [7]. A novel algorithms for blind

watermarking has been introduced to protect the copy right issues and other

multimedia areas, by applying singular value decomposition (SVD) and least square

support vector machine (SQ-SVM) into watermark embedding and detection. In this,

two process are encoding and decoding, in coding process watermark is embed and

in decoding process extract the embedding watermark using support vector machine

[8]. Digital watermark has been proposed for the protection of copyright detection

between seller and buyer. A content owner (seller) inserts the unique watermark into

copy of content before it to be sold. If the buyer sells unauthorized copy of content

then it can be detect using watermark detection algorithm [9]. A joint

encryption/watermarking system with more security based on double random based

(DREP) in quaternion gyrator transform domain is addressed. In this proposed

scheme, a RGB-scale watermark image together is encoded [10]. The spread-

spectrum like discrete cosine transform domain (DCT) watermarking technique for

copyright protection for still digital images. In this technique, DCT is applied on

blocks 8*8 pixels on images as in JPEG algorithm. The watermark can encode

information to protect the illegal misuse [11]. The robustness and the

imperceptibility are the basic requirements of the digital watermarking. The main

key factor which effect the robustness and the imperceptibility is its strength.

Artificial neural network is make its strength [12]. There are two characteristics of

perfect digital watermarking. One is robustness that is geometric attacks which effect

on the image, i.e. image compression. And the other one is preserve the image

quality after embedding the watermark. The requirement robustness and the image

quality is affect to each other. An optimum solution has been found to tackle this

problem that is game theoretic approach. Game theory is formulate the dynamic non-

cooperative complete information in which one player has the complete information

of the other player but it is not necessary the actions [13] .

Fingerprint watermark technique is also used for the protection of copyright

detection. It is also known as watermarking using biometric system. In this process

embedding the fingerprint using the novel method of empirical mode decomposition

(EMD) and gene expression program (GEP) provide together. The watermarking

algorithm uses singular value decomposition (SVD) and lifting based discrete

12

wavelet transform (DWT). This method is more secure, robust and imperceptible

watermarking algorithm. If the logos protection, which are protected by

watermarking biometric scheme which is more protected and secure than the other

watermarking techniques [14].

Wavelet-tree-based watermarking technique is also used for copyright protection.

Host image divided into groups so called supper trees. The watermark is embedded

by quantized supper trees. In this technique watermark is embedded in each bit

frequency domain which is minor modification in original image which is almost

invisible in original image [15].

The watermark is also used in databases to improve the database integrity. Meng-

Hsiun Tsai et al. introduced the database watermark technique for temper detection.

In this technique, there are there steps to complete this algorithms. First, MD5

algorithm is used to fetch database extraction. Second, XOR operation applied on

extraction database and digital watermarking gets a certification number. At last,

encrypt the certification number and makes public in network with database. Before

using this, user needs to use database owner’s database encrypt certification number.

In the last step, if the watermark complete at the end user then it proved the database

integrity [16].

A fragile watermark can also identify where the original image has been altered.

Another watermark is variable-water mark two dimensional algorithm (VW2D) is

also used in this approach [17].

Digital watermarking is also used for files posting on the internet. In the recent years

growing the social networking websites has highlighted the importance of protection

and security of digital products. These digital products are also called digital

contents, such as digital videos, games, computer animation, actions, service

learning, digital publishing, digital publishing, content, software, and network

services. On social networking, people share their post, photos, videos which

demands the security. After analyzed that, it was found that female respondents often

share digital contents than male respondents. So they need more protection than male

respondents. A digital technique is used to tackle this issues on the basis of some

questionnaire. Digital watermarking as a sign with the figures of copyright set to

digital watermarking to set their picture protection. Questionnaire based on personal

13

information, name, age, occupation, sex. After these analysis, the result was showed

that the female and worker sharing content more than other [18].

The camera in smartphones is one of the most widely used functions nowadays.

Millions of people used camera in smartphones in whole world and take pictures,

recording audio and videos and share these digital contents to their families,

relatives, and friends through different social network. This unprecedented uses

produces huge number of multimedia contents such as audio, videos and clips

sharing on social network might create intellectual property [IP] issues. Shared

images, videos and clips on social networks without owner’s permission. To tackle

this issue a digital signature based technique is introduced in which owner’s add

their signature on digital contents. This technique is more efficient and flexible to

protect the digital contents. This watermarking system is recently introduced in

android smartphones, the software is called lollipop [19].

Mobile phone numbers are also used for the protection of digital images captured by

the mobile phones. Because mobile phone number is a unique number all over the

world. Mobile phone number is embedding in the digital image captured by the

owner’s phone Binary coded decimal (BCD) encoder. This research has been

proposed in UAE people in which 14 digit phone number is converted into 4-bit

binary number. Then the checksum of phone number is added to this 4-bit binary

number to generate checksum binary vector. This checksum can be used during the

decoding process to verify the correctness of extracted phone number [20].

14

Chapter 3

Proposed Technique

15

This study is based upon the updated literature review that identified digital

watermarking in multimedia for the protection of copyright. Digital watermarking

algorithm is based on two mathematical techniques. First is Singular value

decomposition (SVD) and interpolation method viz. Linear algebra and cosine

interpolation method.

A cover image and watermark image is decomposed into respective SVD. After this,

the singular value of watermark image is found by using interpolation method. After

this, the inverse SVD (Singular Value Decomposition) is performed. Then various

attacks were applied on digital watermark cover image. After this process the

watermark image was extracted from the distorted image and then compared with

the original image.

3.1 Algorithms:

3.1.1 Watermark embedding algorithm

I. The Singular Value Decomposition was applied on cover image ‘Ic’ and

watermark image on ‘Iw’ as follow:

𝐼𝑐 = 𝑈𝑐𝑆𝑐𝑉𝑐 𝑇 … … … … 𝑒𝑞. (4.1)

IC=UcScVcT ………….eq (4.2)

Iw=UwSwVW

II. Now Sw of watermark image Iw was added to Sc of cover image Ic as:

16

Method-I Method-II

• Uwi= Uc

• Swi=(1-t)Sw+tSc

• Vwi=Vc

• Uwi=Uc

• T1=(1-cos(t*PI)/2

Swi=(1-t1) Sw+t1Sc

• Vwi=Vc

III. After this step, Now the watermarked image Iwi obtained by using Vwi,

Swi and Vwi as follow:

Iwi=UwiSwiVwi T……………………………..eq(4.3)

We also know that the watermarked imaged Iwi was also attacked by

different techniques like blurring, compression, filtering, addition of

noise etc.

3.1.2 Watermark extraction Algorithm

Given Iwi,Iw, t & Ic the embedded watermark was extracted as:

I. The SVD was performed on watermarked image Iwi and watermark

image Iw as

Iwi=Uwi Swi VwiT ………………………….…….eq (4.4)

Iw=Uw Sw VwT ………………………………....eq (4.5)

Ic=Uc Sc VcT …………………………………..eq (4.6)

17

II. After this step now, singular values of extracted watermarked image Iew

is calculated as:

III. Now the watermarked image Iew was obtained by using Uew, Sew and

Vew as:

Iew=Uew Sew VewT…………………………………eq (4.7)

IV. Then the comparison between the original watermarked image Iw with

obtained watermark Iew image is performed by using different operations.

V. After this, the comparison parameters Accurate Rate (AR), Normalized

Correlation (NC), and Peak Signal to Noise Ratio (PSNR) were

calculated.

All these operations are performed for different values for t.

3.2 Research Model

The proposed methodology for this study is best shown in figure 4. The Cover image and

watermark images are decomposed into corresponding singular values. After

decomposition images, then interpolation method is applied to watermark the cover image.

Then the reverse SVD method is applied to produce the watermarked process. Different

attacks have been done over the watermarked image. After this process, we extract the

18

watermark from the attacked watermarked image by using the SVD and reverse

interpolation technique, as best shown in the following flowchart.

Figure 2: Proposed watermarking Scheme

3.3 Programming Tools

I. Java programming environment

II. Matlab

III. JAMA Package

3.4 Working environment

• Dual core CPU 3.00 GHz or higher

• Operating System: Linux, Ubuntu, windows 8,7,64/32 bit

• Minimum 2 GB RAM or higher

• Minimum 50 GB HDD or higher

3.5 Technologies

19

The following technologies have been used during the preceding of this

dissertation:

• Singular Value Decomposition (SVD)

• Interpolation Methods

• Linear Interpolation

• Cosine Interpolation

• Comparison metrics

• Peak Signal to Noise Ratio

(PSNR)

• Normalized Correlation

(NC)

• Accurate Rate (AR)

20

Chapter 4

Performance Measures

21

Performance Measures

The performance measurement of this study is the process of collecting,

analysis and reporting information related to the performance parameters of

an individual, in group, system or a component. Following comparison

metrics are used as:

• Accuracy Rate (AC)

• Normalized Correlation (NC)

• Execution time

• Peak signal to Noise Ratio (PSNR)

4.1 Normalized Correlation (NC)

The Normalized Correlation (NCC) calculates the similarity and dissimilarity

measurement between original watermark and extracted watermark, by using

this formula which is given as:

𝑵𝑪 = ∑

𝑁

𝑖=1

∑ 𝑤(𝒊, 𝒋) ∗ 𝑤′

𝑁

𝑗=1

(𝒊, 𝒋)

∑ ∑ 𝒘𝟐(𝒊, 𝒋)𝑵

𝒋=𝟏𝑵𝒊=𝟏 ……………………….eq (5.1)

In this equation, the N×N is the watermark size, w (i, j) and w’(i, j) represents

the watermark and recovered images watermark respectively.

4.2 Accuracy Rate

In this dissertation, the Accuracy Rate (AR) is used to measurement the

difference between the recovered image and watermark image. The Accuracy

Rate is calculated as follows:

AR= CP/NP ……………………………..eq (5.2)

In this above equation, the NP is the number of pixels in the original

watermark image and CP is the correct pixels obtained by comparing the

pixels of the covered watermark and corresponding original images.

22

4.3 Peak Signal to Noise Ratio (PSNR)

The PSNR is an engineering term for the measurement of signal power. It is

measure the ratio between the maximum power of a signal and power of

corrupting noise that affects the quality of signals [21]. Because there are many

signals have a wide dynamic range, the PSNR is usually expressed in terms of

the log decibel scale. The PSNR is easily defined via mean squared error (MSE).

This formula is easily defined as follows.

….eq (5.3)

…….eq(5.4)

In this equation, MAXi is the maximum possible pixel value. PSNR is help to measure the

imperceptibility of the watermark

4.4 Execution Time

The time is very important part to execute the every interpolation method. The time is

calculated for each interpolation method. It is considered as performance measure parameters

as operation speed of any interpolation method which impacts the choice of the method.

23

Chapter 5

24

SYSTEM VALIDATION SCHEMES

The validation for the system is to validate the system for checking the Imperceptibility and

Robustness. There are two types of validation are given below.

• Imperceptibility Validation

• Robustness Validation

5.1 Imperceptibility Validation

Imperceptibility is the main characteristic of digital image watermarking. The

watermark data must be invisible in watermarked cover image. This validation process

is done by using given measures.

5.1.1 Normalized Correlation

The Normalized correlation calculate the similarity between the watermarked cover

image and original image. Normalized Correlation measure the pixel wise comparison

between watermarked image and original image.

Expected Output:

Normalized correlation give the satisfying output so that the information in cover

image is invisible.

5.1.2 Peak Signal-to-Noise Ratio

The PSNR is technical term, it calculate the ratio between the maximum power of

signal and the corrupting noise signal. The more PSNR, is more similarity the extracted

watermark image.

Expected Output:

PSNR measure the decibel value, it satisfying range of cover image so that it is invisible.

5.2 Robustness Validation

The Robustness Validation is another main characteristic of image watermarking. The

extracted information must be similar to the embedded original watermark image. The

Validation is done by using the given measures as.

5.2.1 Normalized Correlation

The Normalized correlation calculate the similarity measurement between the extracted

watermark image and original embedded watermark image. The NC measure the pixel wise

comparison between the original and watermark images.

25

Expected output:

The correlation give the satisfy output so that the extracted watermark is recognizable.

5.2.2 Peak Signal-to-Noise Ratio

The PSNR is technical term, it calculate the ratio between the maximum power of

signal and the corrupting noise signal. The higher the value of PSNR, the similar extracted

watermark information in image, so that the more PSNR value, it is strong robustness in

image.

Expected output:

The correlation give the satisfy output so that the extracted watermark is recognizable.

5.2.3 Accuracy Rate

The Accuracy Rate (AR) give the difference between the recovered image and original

watermark image. It return the correct number of pixel which is count in total pixels.

Expected output:

The accuracy rate must satisfy the range of watermark image which is recognizable as

compared to the original one watermark image.

5.3 Computation time

The time complexity is very important in every process, so that time is measured for each

interpolation method. Time is measured only for interpolation technique as well as for the

corresponding in reverse process.

26

Chapter 6

27

OBSERVATIONS AND DISCUSSION

The discussions and results obtained from the interpolation operations are show in this

chapter. Details of the obtained results can be shown in appendix section of this dissertation.

Input data as cover image for observation, analyzing and discussion as follows:

a) Test picture for cover and watermark

i. Lena as cover image (low resolution)

ii. Cisco as watermark image (low resolution)

b) Real image for cover and watermark

i. Durbar as cover image (high resolution)

ii. Vanquish avatar as watermark image (high resolution)

Analysis are done on the basis of following factors:

6.1 On the basis of imperceptibility

The imperceptibility is important factor during the addition of watermark. This process is

done by using the following measure.

6.1.1 Obtained Data

I. NC Values

Figure 3: NC for test image [From table 1]

28

Figure 4: NC for real image [From table 2]

II. PSNR Values

Figure 5: PSNR for test image [From table 3]

29

Figure 6: PSNR for real image [From table 4]

6.1.2 Analysis

In this above results, we have found that the cosine method give the better NC and PSNR

values as compared to liner interpolation method. So Cosine interpolation method generate

better results than linear interpolation. It is also applicable for the lower weight of cover

image. In NC and PSNR values and result of watermarked image, we can say that this

method provides good imperceptibility for the cover image. The reason behind this is that

the Singular Value Decomposition method provides better stability to the system under the

decomposition. The cosine method also provides good approximation to the intermediate

values. Ultimate the required imperceptibility has achieved.

6.2 On the basis of Robustness

During the watermark extraction, the robustness factor is also important. Here NC and

PSNR values are calculated for different attacks. This is measured by following results.

6.2.1 Obtained data

I. NC Values

• Without any attack

30



31

• After adding noise



• After rotation


32


• After negation


33


II. PSNR Values




• After adding noise

34



• After rotation


35


• After negation



36

6.2.2 Analysis

From the above graphs of NC and PSNR and extracted watermarked image snaps are included

in appendix, the following results are derived from the above graphs.

• When there is no attack on a watermarked cover image, the extracted is very good and very

similar to the original embedded watermark. This is reflected by corresponding PSNR and

NC values. In this type of case, the two interpolation methods generate similar PSNR and

NC results but the Cosine technique provide good PSNR values, as shown in above graphs.

• When we add salt and pepper noise on watermarked cover image, the extracted watermark

image is recognizable in range 25% to 75% of cover image weight. It is also reflected by

the corresponding values of NC & PSNR, these values are not good but visually extracted

watermark is recognizable. So in this case, the linear interpolation method generate better

NC values whereas the Cosine interpolation method generate good PSNR values.

• When we rotate watermarked cover image at 45 degree, the extracted watermark is good

and recognizable. So, the PSNR & NC values are no so good but visually the extracted

watermarked is recognizable, as shown in the images included in appendix. In this case, the

Cosine methods generate better PSNR & NC results as compared to other methods.

• When we take the negation of the watermarked cover image, the extracted watermark is

very good and better recognizable, although the corresponding PSNR & NC values are not

good. There is also reflected by the images in appendix section. So the negation of the image

has no much effect. All these methods provide similar measure of the performance

parameter, as reflected in above graphs.

In above results are get after implementation of proposed algorithm. The main property of

digital image watermarking is robustness & imperceptibility. This Singular Value

Decomposition (SVD) is based on watermarking technique is able to provide good

imperceptibility to the watermarked cover image but it is also unable to provide robustness

in some attacks. So the extracted watermarked is recognizable, the PSNR & NC values are

low but good in some cases.

6.3 On the basis of computation time

37

6.3.1 Obtained data

Figure 23: Computation time for test image [From table 21]

Figure 24: Computation time for real image [From table 22]

6.3.2 Analysis

The computation time for the both interpolation method are similar. So there are no distinction

in performance of those methods on basis of computation time for two interpolation methods.

But above graphs shown some sharp rise when time used up. It may be just when memory &

CPU of the computer busy on serving another process. Otherwise, both interpolation method

used computation time are similar.

38

CHAPTER 7

39

Conclusion and Recommendation

7.1 Conclusion

Digital image watermarking is the process of embedding the watermark image in the

cover image so that the embedded watermark image can be used to prove ownership and

copyright regarding issues. It has two main characteristics viz. imperceptibility and

robustness. Singular Value Decomposition (SVD) technique has characteristics to resist

changes on its singular values and make the system stable. This property of SVD is used

in this study. Two interpolation methods are used individually to mark the watermark in

the cover image. As we have observed and discussed, “SVD based digital image

watermarking using linear and cosine interpolation method” is very good in providing

imperceptibility to the watermarked cover image. The imperceptibility is possible

because of the stability property of SVD. Though the extracted watermark was good

enough to be recognized, the performance measuring parameters was not good enough

to be considered in some cases. Hence for the robustness, the SVD based image

watermarking needs another transformation before applying SVD decomposition. The

two interpolation methods used were linear and Cosine. The Cosine method provided

better result and parameter values as compared to other interpolation methods. Also time

required by all two methods was similar. Hence, Cosine interpolation method can be

used for adding watermark for better result.

7.2 Limitation of dissertation

• This study does not include for color images.

• The study deals only singular watermarking scheme.

• The targeted cover image file is of 512*512 resolution gray images and the

watermark is of 256*256 resolution gray images.

7.3 Future enhancement and important achievement

From by using this dissertation one can make the watermarking module. S/he can implement

watermarking method based on SVD and Cosine as it was justified in previous chapters. This

is the major achievement of this study. Anyone can improve this work by implementing

40

frequency domain transformations like Discrete Cosine Transform (DCT), Discrete Wavelet

Transform (DWT) for improving robustness of the process. S/he can study the same on color

image. S/he can improve it by including metrics like processor usage, memory usage etc. Also

other interpolation technique can be included.

41

APPENDIX A: Performance Parameters obtained

1. During watermark addition, for imperceptibility

i. NC Values:

Cover weight

Method

0.25 0.35 0.45 0.55 0.65 0.75

Linear 90.61 92.7 93.96 94.17 95.15 96.55

Cosine 89.16 90.3 92.4 94.45 96.14 97.66

Table 1: NC for test image

Table 2: NC for rea

ii. PSNR Values:

Table 3: PSNR for test image

Table 4: PSNR for

Cover weight

Method

0.25 0.35 0.45 0.55 0.65 0.75

Linear 74.61 77.7 81.96 84.17 87.15 91.55

Cosine 70.16 75.3 80.4 85.45 90.14 94.66

Cover weight

Method

0.25 0.35 0.45 0.55 0.65 0.75

Linear 22.61 23.7 25.96 27.17 29.15 31.55

Cosine 21.16 22.3 24.4 27.45 31.14 36.66

Cover weight

Method

0.25 0.35 0.45 0.55 0.65 0.75

Linear 16.61 17.14 19.18 20.03 22.09 25.77

Cosine 15.16 16.12 18.23 21.22 24.66 30.19

42

2. During watermark extraction, robustness

i. NC Values:


Table 5: NC for tes

Table 6: NC for rea

• after noise addition

Table 7: NC for tes

Table 8: NC for rea

Cover weight

Method

0.25 0.35 0.45 0.55 0.65 0.75

Linear 98.65 98.23 98.21 98.88 98.45 98.17

Cosine 98.05 98.56 99.07 98.7 98.69 97.12

Cover weight

Method

0.25 0.35 0.45 0.55 0.65 0.75

Linear 99.12 99.01 98.13 98.54 98.14 98.03

Cosine 99.05 99.56 99.07 98.06 98.61 96.15

Cover weight

Method

0.25 0.35 0.45 0.55 0.65 0.75

Linear 55.01 55.04 54.14 53.54 53.41 52.03

Cosine 55.05 55.56 54.07 53.06 52.61 52.15

Cover weight

Method

0.25 0.35 0.45 0.55 0.65 0.75

Linear 71.01 69.04 67.14 64.54 62.41 60.03

Cosine 72.05 70.56 68.07 64.06 60.61 59.15

43

• After rotation

Table 9: NC for tes

Table 10: NC for r

• After negation

Table 11: NC for test image

Table 12: NC for r

Cover weight

Method

0.25 0.35 0.45 0.55 0.65 0.75

Linear 35.01 40.04 36.14 55.54 68.41 63.03

Cosine 31.05 36.56 36.17 58.05 72.18 57.15

Cover weight

Method

0.25 0.35 0.45 0.55 0.65 0.75

Linear 43.01 45.04 49.14 54.54 59.41 68.03

Cosine 41.05 43.56 47.17 55.05 66.18 57.15

Cover weight

Method

0.25 0.35 0.45 0.55 0.65 0.75

Linear 25.5 26.6 28.15 30.54 34.41 40.03

Cosine 24.05 26.56 28.17 31.05 39.18 56.15

Cover weight

Method

0.25 0.35 0.45 0.55 0.65 0.75

Linear 65.01 63.04 61.14 57.54 54.24 47.05

Cosine 66.05 63.56 62.17 57.05 48.18 36.15

44

ii. PSNR Values:


Table 13: PSNR fo

Table 14: PSNR fo

• After noise addition


Table 16: PSNR for real image

Cover weight

Method

0.25 0.35 0.45 0.55 0.65 0.75

Linear 23.01 27.04 31.14 37.54 37.24 35.05

Cosine 19.05 24.56 30.17 38.05 35.18 30.15

Cover weight

Method

0.25 0.35 0.45 0.55 0.65 0.75

Linear 24.01 30.04 38.14 34.54 30.24 28.05

Cosine 23.05 26.56 39.17 32.05 28.18 26.15

Cover weight

Method

0.25 0.35 0.45 0.55 0.65 0.75

Linear 5.01 5.04 5.14 5.54 5.24 5.05

Cosine 4.05 5.56 5.17 5.05 5.18 5.15

Cover weight

Method

0.25 0.35 0.45 0.55 0.65 0.75

Linear 7.02 7.04 7.24 7.5 7.25 7.09

Cosine 7.05 7.56 7.17 7.05 6.18 6.15

45

• After rotation


Table 18: PSNR fo

• After negation

Table 19: PSNR for test ima

Table 20: PSNR fo

Cover weight

Method

0.25 0.35 0.45 0.55 0.65 0.75

Linear 4.01 4.04 3.14 7.54 9.24 6.05

Cosine 3.05 4.56 3.17 7.05 8.18 6.15

Cover weight

Method

0.25 0.35 0.45 0.55 0.65 0.75

Linear 5.01 5.04 5.14 6.54 7.24 7.06

Cosine 5.06 5.7 5.17 6.05 7.18 7.15

Cover weight

Method

0.25 0.35 0.45 0.55 0.65 0.75

Linear 8.01 8.04 8.14 7.54 7.24 6.05

Cosine 8.05 8.56 8.17 7.05 6.18 4.12

Cover weight

Method

0.25 0.35 0.45 0.55 0.65 0.75

Linear 2.01 3.05 3.15 3.55 3.24 4.05

Cosine 2.06 2.56 3.17 3.05 4.19 6.16

46

1. Execution time

Table 21: executio

Table 22: executio

Cover weight

Method

0.25 0.35 0.45 0.55 0.65 0.75

Linear 392 343 326 361 312 361

Cosine 329 344 345 312 314 343

Cover weight

Method

0.25 0.35 0.45 0.55 0.65 0.75

Linear 361 361 329 343 343 330

Cosine 404 343 343 312 327 327

47

Appendix B: Images Obtained

First the cover image is with watermark and the results are shown below.

48

Figure 25: Embedding watermark using SVD & interpolation

After embedding the watermark in the cover image, various attacks are applied in the

cover watermarked image. The results are shown below.

49

Fig 26: Extracted watermark at t=0.25 using Lena & Cisco

50


51


52

Fig 29: Extracted watermark at t=0.25 using Durbar & Vanquish

53


54


55

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