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LIGHTWEIGHT ROBUST WATERMARKING FOR DIGITAL
IMAGES USING MULTIPLE DOMAINS
THIBA A/P NARASIMHA BHARATHEYAR
DISSERTATION SUBMITTED IN FULFILMENT OF THE
REQUIREMENTS FOR THE DEGREE OF MASTER OF
COMPUTER SCIENCE
FACULTY OF COMPUTER SCIENCE AND INFORMATION
TECHNOLOGY
UNIVERSITY OF MALAYA
KUALA LUMPUR
JULY 2010
ii
UNIVERSITI MALAYA
ORIGINAL LITERARY WORK DECLARATION
Name of Candidate: (I.C/Passport No: ) Registration/Matric No: Name of Degree: Title of Project Paper/Research Report/Dissertation/Thesis ("this Work"): Field of Study:
I do solemnly and sincerely declare that:
(1) (2) (3) (4) (5) (6)
I am the sole author/writer of this Work; This Work is original; Any use of any work in which copyright exists was done by way of fair dealing and for permitted purposes and any excerpt or extract from, or reference to or reproduction of any copyright work has been disclosed expressly and sufficiently and the title of the Work and its authorship have been acknowledged in this Work; I do not have any actual knowledge nor do I ought reasonably to know that the making of this work constitutes an infringement of any copyright work; I hereby assign all and every rights in the copyright to this Work to the University of Malaya ("UM"), who henceforth shall be owner of the copyright in this Work and that any reproduction or use in any form or by any means whatsoever is prohibited without the written consent of UM having been first had and obtained; I am fully aware that if in the course of making this Work I have infringed any copyright whether intentionally or otherwise, I may be subject to legal action or any other action as may be determined by UM.
Candidate's Signature Date
Subscribed and solemnly declared before,
Witness's Signature Date
Name: Designation:
iii
Abstract
Due to rapid growth of the Internet, digital contents such as text, audio, video
and images are easily exchanged and disseminated by worldwide users. As a result,
unauthorized duplication arises and becomes a serious issue which needs to be combat
with. Therefore, several techniques has been researched and developed in guarding
these contents; there are cryptography, encryption, steganography and watermarking.
Out of these solutions, digital watermarking has emerged as a reliable technique in
safeguarding the digital contents; which works on imperceptibly altering an original
digital content to embed a message about the content, which can be later used for
authentication purpose.
Recently, many extensive researches have been carried out in the field of digital
image watermarking due to various types of attacks against the digital images.
Geometrical and image processing operations are two main types of attacks which can
easily distort digital images. In order to resist those attacks, robust and fragile
watermarks are required. A robust watermark is significantly essential in digital image
watermarking whereby the embedded watermark in an image can be easily recovered
even after it has been manipulated with geometrical or other attacks. Fragile watermark
has the ability to detect any unauthorized changes which has been made to the
embedded watermark.
In this dissertation, we introduce a new lightweight robust watermarking
approach based on edge features for spatial domain and wavelet domain. Edge features
are chosen as a key factor of our proposed watermarking technique, since the edge
information is considered to be robust feature which can withstand the scaling attack in
a watermarked image. Moreover, embedding strategy works by embedding watermark
iv
in robust edges of a cover image. Meanwhile, an inverse process of embedding step is
required in the watermark detection stage. In addition, the original cover image is not
needed in validating the detected watermark.
Experiments results demonstrated higher visual quality can be gained in spatial
domain, due to less distortion to the watermarked image. On the other hand, wavelet
domain revealed faster execution time with thresholding technique compared to spatial
domain, which is based on edge detection through convolution approach. By overall
experiment observations, we noticed that the proposed scheme is a simple algorithm
with low computational complexity and consists of moderate image distortions. These
are the three main factors contribute towards the robustness feature of our proposed
scheme. Therefore this scheme turns out to be a lightweight watermarking and it is
suitable to be implemented in many applications.
v
Acknowledgements
I would like to express the deepest appreciation to my dissertation supervisor,
Dr. Woo Chaw Seng, for being supportive throughout my dissertation. This dissertation
would not be completed without his invaluable guidance and comments. I would like to
thank the examiner’s committee member, Dr. S. Raviraja for his encouragement and
insightful comments.
I also like to express my gratitude to my fellow friends and colleagues, specially
to Hairul Aysa , Rajeswari, Lalitha, Noorliza and Farrieza who helped me a lot during
my hard times.
Sincere thanks to my husband Kumara Sastri, mother, sisters and brothers for
their moral and financial supports along the way. To them I dedicate this dissertation.
vi
Table of Contents
Original Literary Work Declaration …....….…..…..……...………………………….ii
Abstract...........................................................................................................................iii
Acknowledgements……..................................................................................................v
List of Figures…………...............................................................................................viii
List of Tables………….. ................................................................................................ix
List of Symbols and Abbreviations................................................................................x
Chapter 1: Introduction .................................................................................................1
1.1 Background ...........................................................................................................1
1.2 Motivation.............................................................................................................1
1.3 Overview of Digital Watermarking ......................................................................2
1.4 Goal and Objectives ..............................................................................................4
1.5 Project Scope.........................................................................................................4
1.6 Dissertation Organization......................................................................................5
Chapter 2: Literature Review........................................................................................7
2.1 Digital Image Watermarking.................................................................................7
2.2 Watermarking algorithm .......................................................................................8
2.3 Watermark properties............................................................................................9
2.4 Watermarking domain.........................................................................................10
2.5 Watermark embedding classification..................................................................11
2.6 Watermark detection techniques.........................................................................12
2.7 Watermarking Applications ................................................................................12
2.8 Robust Watermarking Techniques......................................................................13
2.8.1 Spatial domain..........................................................................13
2.8.2 Transform domain....................................................................14
2.8.3 Scale normalization and flowline curvature.............................17
2.8.4 Features point ...........................................................................18
2.8.5 Support Vector Machine ..........................................................18
2.9 Lightweight watermarking techniques................................................................19
2.9.1 Buyer-seller watermarking protocol ........................................19
2.9.2 Optimal Differential Energy Watermarking of DCT Encoded
Images and Video.....................................................................19
2.9.3 MMS Content Copyright Protection using Watermarking ......19
vii
2.10 Chapter Summary................................................................................................20
Chapter 3: Methodology...............................................................................................22
3.1 Introduction.........................................................................................................22
3.2 Generic Watermarking Process...........................................................................22
3.3 Proposed Watermarking Scheme ........................................................................23
3.3.1 Edge Detection.........................................................................24
3.3.2 Spatial Domain Watermarking.................................................26
3.3.3 Wavelet Domain Watermarking ..............................................30
3.3.3.1. Discrete Wavelet Transform ..............................................30
3.3.3.2. Level-One 2-Dimensional Discrete Wavelet Transform ...32
3.3.3.3. Level-Two 2-Dimensional Discrete Wavelet Transform...37
3.4 Chapter Summary................................................................................................41
Chapter 4: Experiments and Results...........................................................................42
4.1 Introduction.........................................................................................................42
4.2 Experiment Setup................................................................................................42
4.3 Interim Experiment Results.................................................................................43
4.3.1 LiREF watermarking in spatial domain ..................................44
4.3.2 LiREF watermarking in wavelet domain ................................46
4.4 Results Analysis ..................................................................................................47
4.4.1 LiREF watermarking in spatial domain ..................................47
4.4.2 LiREF watermarking in wavelet domain ................................49
4.5 Discussion ...........................................................................................................58
4.6 Overall Analysis..................................................................................................60
4.7 Chapter Summary................................................................................................64
Chapter 5: Conclusion.... ..............................................................................................66
5.1 Summary of the Dissertation...............................................................................66
5.2 Achievements......................................................................................................67
5.3 Future work .........................................................................................................68
References…………...... ................................................................................................69
viii
List of Figures
Figure Page
Figure 3.1 Generic watermarking process ......................................................................22
Figure 3.2 Watermark embedding steps in spatial domain .............................................27
Figure 3.3 Watermark detection steps in spatial domain ................................................29
Figure 3.4 Level-one 2-D DWT decomposed image......................................................31
Figure 3.5 Level-two 2-D DWT decomposed image......................................................31
Figure 3.6 Watermarks embedding steps in Level-one 2-D DWT .................................36
Figure 3.7 Watermarks detections steps in Level-one 2-D DWT...................................36
Figure 3.8 Watermarks embedding steps in Level-two 2-D DWT .................................38
Figure 3.9 Watermarks detection steps in Level-two 2-D DWT ....................................40
Figure 4.1 Cover images used for experiments...............................................................44
Figure 4.2 Edge detections for Lena image ....................................................................45
Figure 4.3 Cover image and its watermarked copy.........................................................45
Figure 4.4 Level-one 2-D DWT watermarking...............................................................46
Figure 4.5 Level-two 2-D DWT watermarking ..............................................................47
Figure 4.6 Comparison of p value among cover images in spatial domain. ..................49
Figure 4.7 p values for H1, V1, D1 sub bands of cover images; Threshold > 10...........51
Figure 4.8 p values for H1, V1, D1 sub bands of cover images; Threshold > 50..........51
Figure 4.9 p values for H1, V1, D1 sub bands of cover images; Threshold > 70..........52
Figure 4.10 p values for H1, V1, D1 sub bands of cover images; Threshold > 90........52
Figure 4.11 p values for H1, V1, D1 sub bands of cover images; Threshold > 130…...53
Figure 4.12 p values for H2, V2, D2 sub bands of cover images; Threshold > 10........55
Figure 4.13 p values for H2, V2, D2 sub bands of cover images; Threshold > 50........55
Figure 4.14 p values for H2, V2, D2 sub bands of cover images; Threshold > 70........56
Figure 4.15 p values for H2, V2, D2 sub bands of cover images; Threshold > 90........56
Figure 4.16 p values for H2, V2, D2 sub bands of cover images; Threshold > 130......57
Figure 4.17 Comparisons p values between Level-one and Level-two 2-D DWT........58
ix
List of Tables
Table Page
Table 4.1 LiREF watermarking experiment results in spatial domain............................48
Table 4.2 LiREF watermarking experiment results in Level-one 2-D DWT .................50
Table 4.3 LIREF watermarking experiment results in Level-two 2-D DWT.................54
Table 4.4 Comparison p values between Level-one and Level-two 2-D DWT.............57
Table 4.5 Execution time, PSNR and MSE in spatial domain........................................61
Table 4.6 Execution time, PSNR and MSE in level-one 2-D DWT...............................61
Table 4.7 Execution time, PSNR and MSE in level-two 2-D DWT...............................62
Table 4.8 PSNR values of recovered watermark from embedding schemes ..................64
x
List of Symbols and Abbreviations
1-D DWT One-Dimensional Discrete Wavelet Transformation
2-D DWT Two-Dimensional Discrete Wavelet Transformation
DCT Discrete Cosine Transform
DCT-SVD Discrete Cosine Transform Singular Value Decomposition
DDWT Distributed Discrete Wavelet Transformation
DFT Discrete Fourier Transform
DHT Discrete Hartley Transform
D-MR-DCT Distributed Multi-Resolution Discrete Cosine Transform
DRM Digital Rights Management
DT-CWT Dual Tree-Complex Wavelet Transform
DWT Discrete Wavelet Transform
DWT-SVD Discrete Wavelet Transform Singular Value Decomposition
ECC Error Correcting Code
Fuzzy-ART Fuzzy Adaptive Resonance Theory
HVS Human Visual System
ICA Independent Component Analysis
IDWT Inverse Discrete Wavelet Transform
JPEG Joint Photographic Experts Group
LiREF Lightweight Robust Watermarking Using Edge Features in Multiple
Domains
LSB Least Significant Bit
MMS Mobile Multimedia Service
MPEG Moving Picture Experts Group
MSE Mean Square Error
xi
NA Not Applicable
NVF Noise Visibility Function
PSNR Peak to Signal to Noise Ratio
RST Rotation, Scaling and Translation
SVD Singular Value Decomposition
SVM Support Vector Machine
1
Chapter 1: Introduction
1.1 Background
Rapid development of computer technologies and the Internet has made the
sharing and duplication of copyrighted material such as digital images, audio, music and
software becomes easier than before (Jun, Chi & Zhuang 2007; Reddy, Prasad & Rao
2009; Wang, Hou & Yang 2009). Moreover, there are many web sites which allows
user to upload or download unauthorized materials easily; which tends to promote
internet content piracy. One of the recent problems is the online leak of the unfinished
version of “X-Men Origins” movie before the film’s release, considered as serious
effect to the film makers as they invested huge amount of money on this blockbuster
movie especially during the economy downturn period (Lisa 2009). Therefore,
copyright protection has become a social issue.
1.2 Motivation
Digital watermarking is a promising solution for protecting copyrighted digital
materials. It permits the owner of digital content to imperceptibly alter an original
digital content to embed a message about the content itself, which can be later used to
differentiate the original from the fake copy (Cox et al. 2008). Besides, it is useful for
assisting the authorities to diminish piracy and to ensure that the copyright holders to
receive their proper payment of royalties for their hard work (ScienceDaily 2008). For
example, record companies such as Sony Music and Universal Music Group embedded
anonymous watermarks into songs so that it can help them to trace the origins of the
illegally copied material (ScienceDaily 2008).
2
Watermarking works better than cryptography in copyright protection as it can
guard content even after it is being decrypted, it also survive from reencryption,
compression, digital-to-analog conversion and file format changes. Watermarking
within digital images is significantly stressed nowadays and many researches have been
performed in this area. Robustness and imperceptibility are two essential requirements
of digital image watermarking techniques (Dinghui, Haixia & Chao 2007). Digital
images can be easily altered from its original form using geometrical and image
processing operations, so robustness feature in image watermarking plays an important
role to authenticate the image. To improve imperceptibility of a watermark, perceptual
similarity between the original and watermarked image should be high.
The main contribution of this dissertation is in lightweight robust digital
watermarking for images. We investigated several robust watermarking and lightweight
watermarking methods for digital image. Next, we developed new robust lightweight
watermarking approaches based on edge features in spatial and wavelet domain.
1.3 Overview of Digital Watermarking
An overview of digital watermarking is briefly stated in this section including
essential terms in watermark, watermarking algorithm, watermark domain, watermark
embedding techniques, watermark properties, watermark detection techniques and its
applications. The detailed explanations on these topics is included in Chapter 2.
The following list explains several essential terms which are widely used in the
field of watermarking (Cox et al. 2008). Some of these terms are adopted in preparation
of this dissertation.
3
� Cover image or host image
The original unaltered image is referred as a cover image, which will hides or
“covers” the watermark or the secret message.
� Watermarked image
This is a cover image which has been embedded with watermark.
� Watermark
This is the secret message in the form of an image or pseudo random binary bits
to be embedded in a cover image.
� Watermark embedding
This is a process of altering and encoding a cover image with watermark.
Outcome of this process is a watermarked image.
� Watermark detection or extraction
This is a process of decoding the watermark from a watermarked image.
Generally, a watermarking algorithm consists of watermark embedding and
watermark detection steps. The embedding and detection processes can be implemented
in either spatial domain or transform domain of a cover image. Meanwhile, the method
of embedding watermark can be classified into visible or invisible watermarking. The
effectiveness of a watermarking algorithm is measured through fidelity, data payload,
robustness, and security properties (Cox et al. 2008; Wang, Hou & Yang 2009; Luo &
Tian 2008). There are two different techniques applied in validating the detected
watermark, such as blind and non-blind detection.
Digital watermark has been widely applied for the purpose of copyright
protection, fingerprinting, copy control, broadcast monitoring, and data authentication
(Wang, Yang & Cui 2008).
4
1.4 Goal and Objectives
The goal of this dissertation is to design and develop a lightweight robust
watermarking technique for digital images.
Following are the objectives in order to achieve the above goal:
� To study and review of existing literatures of robust and lightweight
watermarking for digital images.
� To analysis and design a new algorithm for robust lightweight
watermarking to improve weakness in current literatures.
� To implement, test and evaluate the proposed new algorithm developed.
1.5 Project Scope
The proposed lightweight robust digital watermarking is focused on gray scale
digital images. Watermark embedding and detection are employed through the edge
features of a cover image. The selected domains for our proposed scheme are based on
spatial and wavelet domains. Scaling attack is applied for testing the robustness against
geometrical distortions. A simple and a fast algorithm is employed in order to enhance
the robustness feature in this proposed watermarking approach. Further, this idea tends
to be blind watermarking detection since original image is not required for watermark
validation purpose.
5
1.6 Dissertation Organization
The dissertation is organized as follows. In Chapter 1, we defined watermarking
as a prominent solution for copyright protection, which works better than cryptography
(Cox et al. 2008). Next, a brief explanation on several essential terms used in the field
of watermarking is presented. This is followed by objective and scope definitions for
our proposed watermarking scheme.
Chapter 2 is describes of watermarking algorithm, properties, domain,
embedding classification, detection techniques and its applications. Final section
discussed about several existing robust watermarking methods and lightweight
watermarking methods for mobile platform. The corresponding method’s strengths and
weaknesses are highlighted. At the end, we define and describe a new lightweight
robust watermarking scheme using simple and fast algorithm.
The Chapter 3 explanation starts with the descriptions of generic watermarking
process and continued with discussion on our proposed watermarking scheme for spatial
and wavelet domains. A detailed step has been drawn out for watermark embedding and
detection process. In addition, edge features is employed in developing the scheme and
it is designed for gray scale digital images.
The experiment setting and experiment results of the proposed scheme are
included in Chapter 4. Five different cover images are evaluated during the experiments
and purposely attacked with scaling distortions. Next, the experiments results are
tabulated into tables and charts information. Detailed discussions, comparisons and
overall analysis are performed using this information.
6
Conclusion is made in Chapter 5. This includes the achievements of our
proposed lightweight robust watermarking scheme. Future works are also been
highlighted for further development of this scheme.
7
Chapter 2: Literature Review
2.1 Digital Image Watermarking
Cox et al. (2008) reveals that there is overlapping technical areas among
watermarking, steganography and information hiding. Generally watermarking refers to
the practice of imperceptibly altering a cover work to embed a message about the cover
work (Cox et al. 2008). Steganography is defined as the practice of imperceptibly
altering a cover work to embed a secret message. This secret message is unrelated to the
cover work. Meanwhile information hiding is defined as creating imperceptible
information as in watermarking or keeping the existence of the information secret. So it
can be concluded that watermarking and steganogarphy as the derived approaches from
information hiding.
Watermarking can be applied for both analog and digital media. One of the
analog approaches can be found on paper watermark such as currency notes (Cox et al.
2008), whereby an invisible portrait is embedded directly during the currency note
making process and only becoming visible as a result of a special viewing process.
Besides being invisible, the watermark signifies the authenticity of the note. Digital
watermarking means embedding secret messages within digital media such as text,
audio, video and image and can be extracted using specific algorithm. As commonly
known, digital media can be easily shared over the Internet using various
communication technologies and the watermark can be removed from the content for
the redistributions purpose. Due to this reason, a robust watermark is needed in
preventing unauthorized access to copyrighted digital media in wide ranges of
applications. This research work covers the robust watermark for digital images.
8
Robust watermarking for digital images can be defined as a method of
embedding watermarking signals or codes into digital images which can withstand
against the geometric distortions such as Rotation, Scaling And Translation (RST) and
non-geometric attacks such as Gaussian noise, Gaussian blur, contrast adjustment and
histogram equalization. Although many approaches has been introduced earlier in this
field, but it still shows some techniques severe to geometric attacks and need to be paid
higher attention in improving it.
In this chapter, we reviewed available literatures of robust watermarking
schemes with their related strength and weaknesses in Section 2.7 together with
available literatures in the area of lightweight watermarking for mobile platform in
Section 2.8. Meanwhile the following Section 2.1 to Section 2.6 includes a brief
explanation on digital watermarking from Section 1.2.
2.2 Watermarking algorithm
Watermarking algorithm consists of embedding and detection steps. A good
watermark need to be constructed (Lu, Lu & Chung 2006), before the embedding steps
takes place, since a well structured watermark can improve the watermark embedding
capacity and quality of watermarked image. Once the watermark is constructed, it is
embedded along with a chosen optional key within the cover image through selected
embedding algorithm. In addition, embedding step can works on spatial or frequency
domain. Finally, the end product of embedding step is the watermarked image which
can be classified as either visible or invisible watermark on it. Embedding domain and
its classification is further discussed in Section 2.3 and Section 2.4.
9
Detection is the reverse process of embedding. Further, it is identified as the
process of authenticating the watermarked image. As initial step, the watermarked
image is manipulated accordingly using detection algorithm whereby the embedded
watermark is located and extracted. The extracted watermark is then compared with the
original watermark and key which has been used in earlier in embedding step. In
practice, some algorithm may or may not require the presence of original image which
is referred as either blind or non-blind watermarking. These approaches are further
explained in Section 2.5. Further, a statistical computation is made based on this
comparison in order to verify the existence of watermark. Details of this watermark
algorithm is further discussed in Chapter 3.
2.3 Watermark properties
Watermark properties can be categorized as fidelity, payload, robustness,
security (Cox et al. 2008; Wang, Hou & Yang 2009; Luo & Tian 2008). The following
section describes the above requirements in brief.
a) Fidelity or Imperceptibility
Fidelity refers to the perceptual similarity between the cover image and
watermarked image. The embedded watermark should not degrade the cover
image perceptually; cover image and watermarked version should appear
similar for user’s view.
b) Data Payload
Data payload is defined as the number of watermark bits that can be encoded
in a cover image. The amount of data payload depends on the size of the
cover image. Higher data payload will cause more watermark bits to be
10
encoded in the cover image but in contrast it tends to reduce fidelity and
robustness rate. The amount of information that can be stored in the
watermark is based on application and the embedding-algorithm quality.
c) Robustness
A robustness feature is measured by the capability of extracting watermark
from a watermarked image even after it has gone through geometric or non-
geometric attacks. Higher robustness rate will increase the value of the
watermarked image but the fidelity rate will decrease. In addition, cost of
complexity tends to increase at the same time.
d) Security
A watermarked image is considered as secured if it is able to defeat hostile
attacks. A hostile attack refers to any process specifically designed to thwart
the watermark’s purpose such as unauthorized removal, embedding and
detection.
2.4 Watermarking domain
Two main domain used in digital image watermarking are spatial domain and
transform domain.
a) Spatial domain
In spatial domain, watermarking embedding is done by directly modifying
the pixel value of a cover image and the most common technique is by
altering least significant bit (LSB) of a cover image (Tirkel, Van Schyndel &
Osborne 1994). Overall, embedding watermarks in this particular domain
11
seems to be imperceptible and easy to implement since the computational
complexity is lower compared to other domain or techniques. However it is
not robust and vulnerable to compression, filtering and rotation attacks.
b) Transform or frequency domain
In this approach the selected cover image need to be transformed into
frequency domain using transformation approach (Woo, Du & Pham 2006;
Agarwal & Goyal 2007; Tang & Wang 2008; Liu et al. 2005); Discrete
Cosine Transform (DCT), Discrete Fourier Transform (DFT), Discrete
Wavelet Transform (DWT) or other transform approach. The transform
coefficient is modified with embedded watermarks, as a result, the inverse
transformed image produces the watermarked image. Although this approach
seems to be complicated and derive higher computational costs, it is more
robust against attacks compare to spatial domain.
2.5 Watermark embedding classification
Methods of embedding the watermark are classified into visible or invisible
watermarking. Visible watermarking is a method of embedding visible transparent
image such as company name, copyright, website address, logo or text which is overlaid
on the cover image (Winwatermark n.d.). The process allows the watermark to be
viewed, but still marks it clearly as the property of the owning organization with the
motive of copyrights authentication purpose. Visible watermarks discourage the
unauthorized copying but it still can be removed or altered (Hu, Kwong & Huang 2004)
by intruders. In contrast, invisible watermark refers to imperceptibly embedding
watermark information into a cover image. This is the preferred method among the
researches since it conceal the presence of watermark from naked eye.
12
2.6 Watermark detection techniques
Watermark detection mainly categorized into blind and non-blind techniques.
Blind techniques is employed with watermarked image for watermark detection and do
not require the original image (Jun, Chi & Zhuang 2007; Wang, Hou & Yang 2009;
Dorairangaswamy 2009), on the other hand, non-blind techniques require the original
image (Liu et al. 2005; Nasir, Weng & Jiang 2007; El-Taweel et al. 2005).
2.7 Watermarking Applications
Watermarking has been widely used in various commercial fields; the following
list explains few specific areas where it is being applied (Wang, Yang & Cui 2008; Cox
et al. 2008; Woo 2007).
a) Copyright Protection
� Watermark helps the copyright owners to verify the illegal copies of their
works by embedding the watermark into their digital works. Later, the
successful detection of the watermark can be use to authenticate the
original owner. Besides, any unauthorized removal of the embedded
watermark will degrade the image imperceptibility.
b) Fingerprinting
� A hidden serial number is embedded within the digital material
purchased by a customer, which discourages them from redistributing the
content. It enables the intellectual property owner to identify which
customer broke his license agreement.
13
c) Copy Control
� Copyright owners can control the terms of use of their work with
watermarking, either as copy once, copy many or no copying at all.
d) Broadcast Monitoring
� Broadcast channels such as televisions and radios are monitored through
active monitoring techniques to check, when and whether the content is
transmitted, to verify advertising broadcasts and verify royalty payments,
and catching instances of piracy.
e) Data Authentication
� Watermark is used to detect any unauthorized modification applied on a
cover work. For example, checking for fraud passport photographs.
2.8 Robust Watermarking Techniques
Recently, there are many techniques for robust watermarking which have been
researched and developed to combat the content piracy issues. The following sections
discussed few methods which have been surveyed throughout the dissertation
preparation work. The approaches are mainly categorized under spatial and wavelet
domain.
2.8.1 Spatial domain
Tirkel, Van Schyndel and Osborne (1994), proposed a LSB approach, which
works by converting the corresponding pixels in a cover image into binary bits and
replacing the least significant bits with watermarking bits. The changes made onto least
significant bits do not degrade the visual effects and watermark detection tends to be
14
simple with this approach. However, as a drawback, watermark information can be
detected easily.
Nasir, Weng and Jiang (2007), proposed an algorithm based on embedding
binary image watermark which is permuted using secret code and Gray code. Later this
permuted watermark is encoded four times in different locations on blue components of
the colored cover image. The advantages of this scheme are; robust against various
image processing operations, and secure technique, since watermark extraction can be
done only with a correct key. The drawback is original image need to be presented
during watermark extraction.
Both multi-resolution and spatial domain used for embedding the watermark in
(Chemak, Bouhlel & Lapayre 2007). The embedded watermark is encoded using Error
Correcting Code (ECC) with turbo code. This algorithm is an efficient in embedding
large watermark bits into cover image and contributes good perceptual fidelity in image
after watermarking process.
2.8.2 Transform domain
An original image is transformed into wavelet domain and watermarks are
embedded in the difference value of the original image and referenced image (Liu et al.
2005), to overcome the watermark problem in spatial domain. This approach survived
various image processing attacks but it seems to be time consuming to do the
embedding process. In addition, original image need to be presented in the
watermarking extraction process. Meanwhile, Jun, Chi and Zhuang (2007), attempted a
randomly encrypt watermark before embedding it into middle sub-band of original
image in wavelet domain. Noise Visibility Function (NVF) has been adopted in
15
identifying the strength of watermark, and Independent Component Analysis (ICA)
algorithm used in watermark extraction whereby it eliminates the need of information
about original image or original watermarks. This approach is robust against Joint
Photographic Experts Group (JPEG) compression, additive noise, and filtering. In
contrast, this approach requires higher computational time, since an additional algorithm
is required in randomly encrypting watermark by scrambling for watermark embedding
process and furthermore, the watermark need to be descrambled and de extended for
watermark extraction purpose.
Another way of enhancing the robustness of a watermark is by decomposing
original image into low-frequency and mid-frequency bands in different resolution
levels by DWT. In (Luo & Tian 2008), the same watermark signals will be embedded
into both frequencies bands. Therefore, they are robust against image distortions and
noise adding, these techniques are vulnerable to some kind of attacks. Meanwhile,
Wang, Chang and Pan (2006), presented a DWT-based robust watermarking scheme
with Fuzzy Adaptive Resonance Theory (Fuzzy-ART). The sensitivity of Fuzzy-ART to
noise and outliers has been resolved by the robustness of the low-low frequency sub-
image. The strengths of this scheme are robust against common image processing and
geometric attacks, blind watermarking and lossless scheme. Bhatnagar and Raman
(2009) make use of gray scale logo image instead of randomly generated Gaussian noise
type watermark. This is done by transforming the original image into wavelet domain
and a reference sub-image is formed using directive contrast and wavelet coefficients.
Later the watermark embedded into reference image by modifying the singular values of
reference image using the singular values of the watermark. This scheme can resists the
ambiguity attack such as removal of watermark.
16
Agarwal and Goyal (2007), proposed an idea of embedding watermark codes
either in Discrete Hartley Transform (DHT) domain or in DCT domain based on the
number of edges in the blocks of the original image. This idea works by embedding the
watermark as block by block in different blocks of an original image depending on the
number of edges found in a given block in the image. Furthermore, the threshold
number of edges in the original image acts as a key in this algorithm and is used in the
watermark embedding and extraction process. This technique is robust against the
various attacks like JPEG compression, random and impulse noises, and cropping.
Robust watermarking based on invariant domain technique with blind
watermarking was introduced by Woo, Du and Pham (2006), eliminate the need of
image resynchronization. Enhanced with Human Visual System (HVS) masking
property of Dual Tree-Complex Wavelet Transform (DT-CWT) subbands, this scheme
defeat several image attacks includes RST operations, JPEG compression, and local
geometrical distortions.
Watermarking scheme based on Singular Value Decomposition (SVD) utilized
in (Lin et al. 2008; Bhatnagar & Raman 2007). In (Lin et al. 2008), SVD and
Distributed Discrete Wavelet Transformation (DDWT) are employed for watermark
embedding purpose. Therefore, this proposed scheme is robust and able to resists
geometric and signal processing attacks. Bhatnagar and Raman (2007), tried with
decomposing and transforming a cover image into four frequencies sub-bands using
Distributed Multi-Resolution Discrete Cosine Transform (D-MR-DCT) and applied
with SVD. Later the singular values of every sub-band are altered with singular values
of the watermark. Experiments proved in this method to be simple, lower complexity
and computational cost. In addition it is tend to be robust than Discrete Cosine
17
Transform Singular Value Decomposition (DCT-SVD) and Discrete Wavelet Transform
Singular Value Decomposition (DWT-SVD) methods.
Fu, Shen and Shen (2005), implemented a robust watermarking through
embedding watermarks in DFT domain in 4 subsampled images. Experiments shows
that, this technique is robust against some geometric and image processing operations.
A significant watermark embedding process is done into perceptually significant
wavelet coefficients using pixel wise masking (Mankar 2008). Robustness is achieved
by encoding the watermark for several times into the detail sub bands. It is
imperceptibly prominent to the HVS.
2.8.3 Scale normalization and flowline curvature
Woo, Du and Pham (2005), proposed a geometrically robust watermarking
based on scale normalization and flowline curvature. Watermark synchronization has
been adopted in an effective manner whereby the scale, translation invariance are
accomplished through scale normalization and meanwhile the rotation invariants
presented with selected feature points in flowline curvature calculations. It has been
experimented by selecting only two corners in recovering an image that underwent RST
transformation and has been verified to be robust against these RST operations.
Meanwhile the searching process becomes shorter since user needs to work with only
four robust corners in image recovery operations and it contributes a lower
computational complexity but tend to be weak against local transformations. Besides,
the original image needs to be presented in the process of watermark detection.
18
2.8.4 Features point
In (Tang & Wang 2008), Harris-Laplace, detector is used in detecting the feature
based corners and further enhanced. Annular region is extracted from this corners
followed by DCT and watermark embedding. An experiment shows this proposed
algorithm robust against several image operations and geometric distortions. A blind
image watermarking algorithm presented in (Wang, Hou & Yang 2009) by extracting
the feature point using a multi-scale Harris-Laplace detector. The resilience of the
watermarks against attacks enhanced by applying pseudo-Zernike moments. This
watermarking scheme able to resist geometric attacks and common image processing
operations. However, this scheme only allows lesser watermark bits to be encoded and
more time consuming in extracting feature point. Wang, Hou and Wu (2008), proposed
a scheme based on scale space theory, with the combination of image feature extraction
and image normalization. Feature points extraction is performed using Harris-Laplace
detector. This scheme tends to be invisible watermarking and able to resist common
signals processing and general geometric attacks.
2.8.5 Support Vector Machine
In (Wang, Yang & Cui 2008), a robust watermarking scheme is establish by
applying classification techniques based on Support Vector Machine (SVM). Besides,
the capacity of watermark embedding is improved greatly without adding extra
template. Proposed scheme proved to be robust against signals processing and
desynchronization attacks such as rotation, scaling, translation, row or column removal,
and local random bend.
19
2.9 Lightweight watermarking techniques
2.9.1 Buyer-seller watermarking protocol
Wu and Pang (2008), proposed a novel buyer-seller watermarking by generating
two independent watermarks W and W’, where W based on fingerprint of digital
contents by seller and W’ based on description by buyer. Buyer generates fingerprint
and matches with fingerprint supplied by seller for verification. This protocol is
efficient in terms of computation cost and communication overhead. Due to this, it is
feasible to be implemented for online application such as with mobile devices.
2.9.2 Optimal Differential Energy Watermarking of DCT Enc oded Images and
Video
In (Langelaar & Lagendijk 2001), a watermarking algorithm is proposed for
real-time watermarking of JPEG or Moving Picture Experts Group (MPEG) streams
based on DCT blocks. This technique avoids the need for decoding JPEG or MPEG
encoded information. Therefore, it is a lightweight watermarking process suitable for
consumer products.
2.9.3 MMS Content Copyright Protection using Watermarking
A Digital Rights Management (DRM) solution for Mobile Multimedia Service
(MMS) messages that uses a centralized approach and watermarking technology are
discussed in (Silva et al. 2003). The advantages of this scheme are able to detect and
avoid unauthorized dissemination of copyrighted content. Moreover this scheme is
robust to scaling attack, efficient in computational cost and provides full interoperability
between mobile phones.
20
2.10 Chapter Summary
A detail description of watermarking algorithm, properties, domain, embedding
classification, detection techniques and its applications is identified. In addition, several
existing literatures of robust and lightweight watermarking for digital images is studied
and reviewed carefully.
Digital image watermarking plays an important role in image authentication and
more prevalent compare to other techniques. Robustness is the crucial part in image
watermarking whereby the watermark resists the common image processing and
geometrical attacks. Therefore, many researches and ideas have been established
towards the field of robust watermarking.
We observed several weaknesses in some of the existing robust watermarking
schemes which have discussed in above sections. Those weaknesses are as the
following. In scheme (Woo, Du & Pham 2005; Liu et al. 2005; Nasir, Weng & Jiang
2007), original cover image need to be presented during watermarking extraction.
Watermark embedding consume much CPU time (Liu et al. 2005; Wang, Hou & Yang
2009). Lesser embedded watermark volume in cover image (Wang, Hou & Yang 2009)
and requirement of additional encryption technique for watermark (Jun, Chi & Zhuang
2007). Besides, we have also identified several strengths from the above discussions.
First, embedding watermark along edge is able to enhance the imperceptibility of
watermark (Agarwal & Goyal 2007). Second, a robust watermarking scheme is able to
defeat geometrical distortions such as scaling attack (Woo, Du & Pham 2006). Third, a
lightweight watermarking with efficient computational cost is suitable for mobile
platform.
21
Based on the above mentioned strengths and weaknesses, we decided to
develop a lightweight robust watermarking for digital image using robust edge features.
The robustness of our proposed scheme is improved by simple and fast watermarking
algorithm. This scheme is experimented separately in both spatial and wavelet domains.
Further explanation on our proposed scheme is included in Chapter 3.
22
Chapter 3: Methodology
3.1 Introduction
There are many robust watermarking schemes for digital images have been
researched and developed recently. Each of these approaches has their unique methods
and approaches to resist the geometrical and image processing attacks. In this chapter,
the general watermarking process is described briefly and followed with the detailed
discussion on the proposed scheme.
3.2 Generic Watermarking Process
Essentially, watermarking approach consists of embedding, and detection
phases. The generic idea has been illustrated in Figure 3.1 (Cox et al. 2008).
Embedder
Algo
Detector
Decision
(Yes/ No)
Watermark,
W
Start
Cover
image, I
Watermarked
image, I’
Detected
watermark,
W’
Stop
Figure 3.1 Generic watermarking process
23
As seen in Figure 3.1, an image is selected to be as the cover image, I, which
needs to be embedded with watermark. Watermark, W may consist of small image or
generated from pseudo random series consisting 1, and 0 bits. An embedder algorithm is
applied during embedding process; which will specifically select the regions to embed
the W in cover image. Watermarked image, I’ , is derived after the completion of
embedding process.
During watermark detection stage, a detector algorithm will work on image I’ , to
detect the watermark which has been embedded earlier. The detected watermark is
identified as W’. W and W’ is compared to verify the similarity and existence of
watermark in I’ .
As mentioned earlier in Section 2.9, we have designed and developed a new
robust watermarking algorithm which is simple and fast. Section 3.3 explains the
proposed scheme in detail.
3.3 Proposed Watermarking Scheme
An essential principle of watermarking is to embed the watermark into
significant regions of a cover image whereby it can withstand the image processing
attacks. Taking this into consideration, we employed a similar approach to conventional
LSB (Tirkel, Van Schyndel & Osborne 1994) image watermarking, as known as
lightweight robust watermarking using edge features in multiple domains (LiREF).
LiREF algorithm is built with edge detection technique in developing the watermarking
system. This idea is fine tuned for gray scale digital images, it can be extended for
colour image watermarking.
24
LiREF watermarking embed watermark into robust edge features of the cover
image, which is less imperceptible to human eyes. On the other hand, an inverse process
of embedding step is required in the watermark detection stage. We implemented this
approach both in spatial and wavelet domains of the cover image. Simple and fast
algorithm is developed using MatLab software to enhance the robustness of LiREF
scheme. In addition, to test the robustness of this scheme against image attacks, the
watermarked image is purposely distorted with scaling attack.
In the next section, we briefly describe the edge detection and scaling attack
which is implemented in the proposed scheme. In addition, we also provide some brief
explanation about MatLab software. Further, we include the detailed discussions about
proposed watermarking implementation steps.
3.3.1 Edge Detection
Edges of an image mainly consist of meaningful and significant information.
Edge detection is a process of locating an edge of an image (Vincent & Folorunso 2009)
which is more meaningful and easier to analyze (Wikipedia, The Free Encyclopedia
2010a). In the proposed scheme, robust edges of a cover image are essential for
watermark embedding and detection process. Moreover, edge information is considered
to be robust reference points which can withstand the scaling attack in a watermarked
image. The robust edges are identified using edge detection operators such as Canny,
Roberts, Prewitt and Sobel and thresholding technique (Neoh & Hazanchuk 2004;
Senthil & Bhaskaran 2008). These two techniques are discussed as the following.
25
a) Edge Detection Operators
Roberts, Prewitt and Sobel are classified as gradient-based edge detectors which
represent edges by determining the level of variance between different pixels
(Neoh & Hazanchuk 2004). On the other hand, Canny edge detector works by
determining the optimum edges by minimizing error rate, well localized and
only one response to a single edge (Senthil & Bhaskaran 2008). Hence it is
known as optimal detector. In the proposed scheme, we tested all four edge
detectors separately in spatial domain in order to determine the robust edges in
cover image and also to identify the best edge detection operator for a particular
cover image.
b) Thresholding
Threshold techniques are used to detect edges in an image by making decisions
based on local pixel information (Teller 1996), it is classified as simple and an
efficient methods (Huang & Bai 2008). In this proposed scheme, a minimum
threshold value based on transform coefficients is chosen for watermark
embedding at level-one and level-two in the wavelet domain. We manually set a
group of threshold consisting 10, 50, 70, 90, and 130 coefficient or pixel values.
All these threshold values are tested with different cover image at level-one and
level-two separately. The image coefficients which are lower than the threshold
is being filtered out and watermark embedded in remaining regions. The best
threshold value is identified by looking at the watermark embedding and
detection performance. More robust edges are revealed and more watermark bits
are able to be embedded through this technique.
26
c) Scaling Attack
Scaling attack is a kind of geometrical attack which works by scaling up or
down the watermarked image. Scaling up enlarge image size, meanwhile in
scaling down, image is being shrunk. In this scheme, we scaled down cover
image into 256 x 256 pixels dimensions for watermark embedding. Further, it is
restored as it original dimensions. For watermark detection, the scaled up image
is resize back into 256 x 256 pixels dimensions. We employed imresize function
in MatLab to perform scaling process.
d) MatLab Software
MatLab is a fourth-generation programming language integrated with interactive
and numerical computing environment (Wikipedia, The Free Encyclopedia
2010b; The MathWorks 2009b). It is developed by The MathWorks, Inc.
MatLab allows matrix-based manipulation for scientific and engineering use,
plotting of functions and data, implementation of algorithms, creation of user
interfaces, and interfacing with programs in other languages (Wikipedia, The
Free Encyclopedia 2010b).
3.3.2 Spatial Domain Watermarking
At first, our proposed LiREF watermarking is tested for cover image in the
spatial domain as this domain is far less complex as no transform is required (Hameed,
Mumtaz & Gilani 2006). Embedding and detection strategies are clearly described as
the following.
27
a) Watermark Embedding
As seen in Figure 3.2, the first step in embedding process is to read in the cover
image which needs to be watermarked and it is identified as, I. Image I is being
normalized, which means it is fixed into standard size by transforming into 256 x 256
pixels size.
Start
Cover
image, I
Normalisation
of image size
into 256 x 256
pixels
Edge detection
Read pixel along
the edge
Embed bit-8
watermark at
bit-8 location of
pixel
Restore original
image
dimension
Watermarked
image, I’
Stop
Count number
of embedded
bits
Embedded
bits, M
Watermark,
W
End of edge
Yes
No
Got to next edge
Figure 3.2 Watermark embedding steps in spatial domain
28
Edge detection operator such as Canny, Roberts, Prewitt and Sobel are applied
separately on I, in detecting the robust edges of the image. Once the robust edges are
detected, the pixel values along these edges being accessed and converted to binary
values. In addition, the watermark is also converted to binary values. Later, the bit-8 of
watermark is embedded into bit-8 location of the pixel value. The design approach for
generating watermark bits is using pseudo random sequence as series 1, and 0 bits. It is
fixed as 128 bits of length. The embedding process starts from top left edge of the most
robust edge detected in earlier step and move on to right direction of the robust edge.
On the other hand, number of embedded bits is being total up and identified as M.
Embedding process is terminated when all the robust edges in I has been watermarked.
Finally, image I will be restored into original dimensions. The scaling attack
takes place here. The output of the embedding process is the watermarked image, I’ .
b) Watermark Detection
The first step in this detection stage works similar as the normalization step in
embedding stage, refer Figure 3.3. Watermarked image, I’ is being normalized into
standard size by transforming into 256 x 256 pixels size. The same edge detection
operator which is used in embedding stage earlier, is used in detecting the robust edges
of the normalized image, I’ . Then, the pixel values along these robust edges are
extracted and converted to binary values. In addition, the watermark is also converted to
binary values. The bit-8 of each extracted pixel value is compared with the bit-8
watermark, W. Number of match is counted and defined as detected watermark bits, E.
This process works for all the robust edges in I’ .
29
Percentage match, p between embedded watermark and detected watermark is
calculated as given by the equation (1):
100*/ EMp = ………………… (1)
where M is total number embedded watermark bits and E is total number of
detected watermark bits.
Start
Watermarked
image, I’
Normalisation
of image size
into 256 x 256
pixels
Edge detection
Read pixel along
the edge
Compare bit-8
pixel value with
bit-8 watermark
Watermark,
W
Detcted
watermark
bits, E
Calculate
percentage
match, p
Stop
Embedded
bits, M
Detected
watermark
bits, E
Percentage
match, p
End of edge
Go to next edge
Yes
No
Count number
of detected bits
Figure 3.3 Watermark detection steps in spatial domain
30
3.3.3 Wavelet Domain Watermarking
Watermarking in wavelet domain has higher robustness compare to spatial
domain (Jun, Chi & Zhuang 2007; Hameed, Mumtaz & Gilani 2006). This is achieved
through the processes called as wavelet transform and inverse wavelet transform.
Wavelet transform decompose a cover image from its spatial domain into different sub
band frequencies of transform domain. Embedding of watermark into sub band
coefficients reveals higher imperceptibility to HVS. On the other hand, inverse wavelet
transform constructs the embedded watermark to be randomly disseminated throughout
the image. Hence, the watermark information becomes inaccessible and more robust
against any unauthorized alterations.
In this proposed approach, we employed LiREF watermarking into a cover
image at level-one and level-two of its wavelet domain using Two-Dimensional
Discrete Wavelet Transformation (2-D DWT) and Inverse Discrete Wavelet Transform
(IDWT). Brief introduction to 2-D DWT and IDWT are included in following sections.
3.3.3.1. Discrete Wavelet Transform
One-Dimensional Discrete Wavelet Transformation (1-D DWT) decomposes all
the rows signals in a cover image into high frequency and low frequency sub bands.
Meanwhile, by repeating 1-D DWT into column signals of same cover image, four
frequencies coefficients sets are created. The four frequencies coefficients denoted as
one low pass sub band, approximation (A1), and three high pass sub bands, which are
horizontal (H1), vertical (V1) and diagonal (D1) as shown in Figure 3.4. Hence, this is
known as level-one 2-D DWT.
31
Figure 3.4 Level-one 2-D DWT decomposed image
A1 coefficients consist of the most information of the cover image, while the
other three coefficients contain the edge and textures components (Jun, Chi & Zhuang
2007; Jin & Peng 2006). Therefore, embedding watermark into A1 coefficients are very
sensitive to the HVS (Reddy, Prasad & Rao 2009). Due to this, we proposed the
watermark to be embedded into three high pass sub band coefficients, to improve the
robustness of watermarking. All four coefficients are reconstructed back into original
image using IDWT; this is inverse process of DWT.
In order to perform level-two 2-D DWT, sub band A1 in level-one 2-D DWT is
further decomposed into next level, consisting of one low pass sub band coefficients,
and three high pass sub bands coefficients, denoted as approximation (A2), horizontal
(H2), vertical (V2) and diagonal (D2). Figure 3.5 illustrates the level-two 2-D DWT
decomposition. As watermark embedding in A2 sub band may degrade the image,
alternatively the watermark is embedded in H2, V2 and D2 sub bands.
Figure 3.5 Level-two 2-D DWT decomposed image
32
In proposed scheme, the DWT function in MatLab can perform level-one and
level-two 2-D DWT with a Daubechies wavelet decomposition filter (The MathWorks
2009a). The following sections discussed LiREF watermarking algorithm for wavelet
domain in further detail.
3.3.3.2. Level-One 2-Dimensional Discrete Wavelet Transform
a) Watermark Embedding
This approach works by embedding watermark bits in cover image in its wavelet
domain at level-one. During the embedding process as seen in Figure 3.6, the cover
image is being normalized its size into 256 x 256 pixels. Normalized image is further
decomposed into level-one DWT using the Daubechies filter. This process will
constructs four sub bands coefficients denoted as A1, H1, V1 and D1. The watermark
embedding is done at H1, V1 and D1 sub band, excluding the A1, since embedding at
this region will degrade the image quality (Jun, Chi & Zhuang 2007). Besides, HVS is
sensitive to the changes in low frequency regions compare to high sub bands (Jin &
Peng 2006).
Coefficients at H1, V1, D1 sub bands are converted from real number values
into 8-bits unsigned integer format. Next, robust edges in H1, V1 and D1 sub bands are
determined using thresholding technique. This task is done as described in Section
3.3.1(b). Coefficients which are above threshold value, T are selected and converted to
binary values. In addition, the watermark is also converted to binary values. The bit-8 of
selected coefficients in H1, V1, and D1 sub bands are substituted with the bit-8 of
watermark. The design approach for generating watermark bits is using pseudo random
sequence as series 1, and 0 bits and it is fixed as 128 bits of length. The substitution
33
process repeats until all the selected coefficients are substituted with watermark. Once
the substitution is completed, all coefficients at H1, V1, D1 sub bands are converted
back into real number format. All four sub bands (A1, H1, V1 and D1) are inversed
transform using IDWT function to retrieve the spatial domain of the image. The image
is normalized into its original size. Finally the watermarked image, I’ has been created.
34
Figure 3.6 Watermarks embedding steps in Level-one 2-D DWT
Start
Cover image, I
Normalisation of image size into 256 x 256
pixels
Level-one 2-D DWT
Normalisation coefficients
into unsigned 8-bit integer
Normalisation coefficients
into unsigned 8-bit integer
Normalisation coefficients
into unsigned 8-bit integer
Pick a threshold value, T
Pick a threshold value, T
Pick a threshold value, T
Embed bit-8 watermark into
bit-8 of coefficient Watermark,
W
Normalisation coefficients
into real number
IDWT
Restore original image
dimension
Watermarked image, I’
Stop
Low pass sub band,
A1
High pass sub band,
H1
High pass sub band,
V1
High pass sub band,
D1
Count number of embedded
bits
Embedded bits in H1,
M
Embedded bits in V1,
M
Embedded bits D1, M
coefficient > T
Yes
Go to next coefficient
No
Yes
Embed bit-8 watermark into bit-
8 of coefficient
Normalisation coefficients
into real number
Count number of embedded
bits
coefficient > T
Yes
Go to next coefficient
No
Yes
Embed bit-8 watermark into bit-
8 of coefficient
Normalisation coefficients
into real number
Count number of embedded
bits
coefficient > T
Yes
Go to next coefficient
No
Yes
End of coefficient
End of coefficient
End of coefficient
35
b) Watermark Detection
The watermarked image, I’ is accessed for the detection stage, refer Figure 3.7.
Besides, the watermark, W and threshold value, T which has been used in embedding
stage is needed for detection purpose. Image I’ is normalized from its original size into
256 x 256 pixels. Next, it is decomposed into level-one 2-D DWT constructing four
frequencies sub bands; A1, H1, V1, and D1.
All coefficients at the high pass sub bands are normalized from real number
values into 8-bits unsigned integer format and those coefficients which are same value
as or above threshold, T are selected and converted to binary values. In addition,
watermark is also converted to binary values. During the detection process, the bit-8 of
each selected coefficient in H1, V1, and D1 sub bands are being read and compared
with the bit-8 watermark, W. Then, the number of match is counted and defined as
detected watermark bits, E. This process works for all the selected coefficients at high
pass sub bands. Finally, a percentage match, p for H1, V1 and D1 sub bands is
calculated based on equation (1), in Section 3.3.2(b).
37
3.3.3.3. Level-Two 2-Dimensional Discrete Wavelet Transform
a) Watermark Embedding
Cover image, I is accessed and normalized into 256 x 256 pixels size, refer to
Figure 3.8. Normalized image is further decomposed into level-one 2-D DWT. This is
followed by level-two decomposition at A1 sub band coefficients, which will constructs
the A2, H2, V2, and D2 sub bands coefficients.
Coefficients at H2, V2, D2 sub bands are converted from real number values
into 8-bits unsigned integer format. Next, robust edges in H2, V2 and D2 sub bands are
determined using thresholding technique. This task is done as described in Section
3.3.1(b). Coefficients which are above threshold value, T are selected and converted to
binary values. In addition, the watermark is also converted to binary values. The bit-8 of
selected coefficients in H2, V2, and D2 sub bands will be substituted with the bit-8 of
watermark. The design approach for generating watermark bits is using pseudo random
sequence as series 1, and 0 bits. It is fixed as 128 bits of length. The substitution process
repeats until all the selected coefficients are substituted with watermark.
Once the substitution is completed, all coefficients at H2, V2, D2 sub bands are
converted back into real number format. Then, A2, H2, V2 and D2 sub bands are
inversed transformed into level-two wavelet domain by employing the IDWT function.
Output of this process is the A1 sub band. A1 sub band together with H1, V1, D1 sub
bands are inversed transform again, in order to retrieve the spatial domain of the image.
Further, the image is normalized into its original size. Finally the watermarked image
has been created denoted as I’ .
38
Start
Normalisation
of image size
into 256 x256
pixels
Level-one 2-D
DWT
Normalisation
coefficients
into unsigned
8-bit integer
Restore
original image
dimension
Watermarked
image, I’
Stop
Low pass
sub band,
A1
High pass
sub band,
H1
High pass
sub band,
V1
High pass
sub band,
D1
Low pass
sub band,
A2
High pass
sub band,
H2
High pass
sub band,
V2
High pass
sub band,
D2
Normalisation
coefficients
into unsigned
8-bit integer
Normalisation
coefficients
into unsigned
8-bit integer
Cover
image, I
Level-two 2-D
DWT
Level-one 2-D
IDWT
Low pass
sub band,
A1
Pick a
threshold
value, T
Pick a
threshold
value, T
Pick a
threshold
value, T
Embed bit-8
watermark into
bit-8 of coefficient
Watermark,
W
Normalisation
coefficients
into real
number
Level-two 2-D
IDWT
Count number
of embedded
bits
Embedded
bits in H2,
M
Embedded
bits in V2,
M
Embedded
bits D2, M
coefficient > T
Yes
Go to next coefficient
No
Yes
Embed bit-8
watermark into bit-
8 of coefficient
Normalisation
coefficients
into real
number
Count number
of embedded
bits
coefficient > T
Yes
Go to next coefficient
No
Yes
Embed bit-8
watermark into bit-
8 of coefficient
Normalisation
coefficients
into real
number
Count number
of embedded
bits
coefficient > T
Yes
Go to next coefficient
No
Yes
End of
coefficient
End of
coefficient
End of
coefficient
Figure 3.8 Watermarks embedding steps in Level-two 2-D DWT
39
b) Watermark Detection
I’ is accessed and normalized into 256 x 256 pixels, refer Figure 3.9. Normalized
image is decomposed into level-one 2-D DWT and followed by level-two
decomposition at A1 sub band coefficients, which creates the A2, H2, V2, D2 sub bands
coefficients.
All coefficients at H2, V2 and D2 sub bands are normalized from real number
values into 8-bits unsigned integer format and those coefficients which are same value
as or above the threshold, T are selected and converted to binary values. In addition,
watermark is also converted to binary values. During the detection process, the bit-8 of
each selected coefficient in H2, V2, and D2 sub bands are being read and compared
with the bit-8 watermark, W. Next, the number of match is counted and defined as
detected watermark bits, E. This process works for all the selected coefficients at high
pass sub bands (H2, V2 and D2). Finally, a percentage match, p for H2, V2 and D2 sub
bands is calculated based on equation (1), in Section 3.3.2(b).
40
Start
Normalisation
of image size
into 256 x 256
pixels
Level-one 2-D
DWT
Normalisation
coefficients
into unsigned
8-bit integer
Low pass
sub band,
A1
High pass
sub band,
H1
High pass
sub band,
V1
High pass
sub band,
D1
Low pass
sub band,
A2
High pass
sub band,
H2
High pass
sub band,
V2
High pass
sub band,
D2
Normalisation
coefficients
into unsigned
8-bit integer
Normalisation
coefficients
into unsigned
8-bit integer
Watermarked
image, I’
Level-two 2-D
DWT
Compare bit-8
coefficient with
bit-8 watermark
Watermark,
W
Stop
Threshold
value, T
Detected
watermark
bits, E
Detected
watermark
bits, E
Yes
Calculate
percentage match,
p
Calculate
percentage match,
p
Calculate
percentage match,
p
Percentage
match, p
Go to next coefficient
Percentage
match, p
Percentage
match, p
Embedded
bits in H1,
M
Embedded
bits in V1,
M
Embedded
bits in D1,
M
coefficients >= T
End of
coefficient
Yes
No
Count number of
detected bits
Detected
watermark
bits, E
Compare bit-8
coefficient with
bit-8 watermark
Yes
Go to next coefficient
coefficients >= T
End of
coefficient
Yes
No
Count number of
detected bits
Compare bit-8
coefficient with
bit-8 watermark
Yes
Go to next coefficient
coefficients >= T
End of
coefficient
Yes
No
Count number of
detected bits
Figure 3.9 Watermarks detection steps in Level-two 2-D DWT
41
3.4 Chapter Summary
A detailed description of watermarking generic process, and proposed LiREF
watermarking for spatial and wavelet domain is explained.
A lightweight robust watermarking based on robust edge features to withstand
the geometric attacks is designed for both spatial and wavelet domains of a cover image.
The proposed scheme is basically derived from the LSB approach with the combination
of edge detection technique. Robust edges become the essential features in identifying
the best location for watermark embedding and more robust to image attacks. Moreover,
this proposed algorithm appears to be simple and has low computational complexity,
which means it is reliable to be implemented on the platform of mobile device.
42
Chapter 4: Experiments and Results
4.1 Introduction
The proposed LiREF watermarking algorithm is experimented and results are
presented through a careful analysis and observations. In order to accomplish this, the
proposed algorithm is tested based on the strategies presented in this dissertation.
Primarily, the algorithm is tested separately in spatial and wavelet domain of TIFF kind
of gray scale images.
4.2 Experiment Setup
a) Hardware Specifications
The following are minimum hardware requirements for the experiment:
i) Processor : Intel Pentium-IV compatible PC
ii) Memory : 1 GB of RAM
iii) Hard Drive : 20 GB hard disk or higher
iv) Display : LCD Monitor (1024 x 768) or higher resolution monitor
b) Software List
The following software is used for the implementation of the proposed
idea:
i) Operating System : Windows XP
ii) Development tool : MatLab R2008a
iii) Runtime environment: Windows XP and above or Windows
Server 2003.
43
c) Image data configuration
The proposed idea is developed and evaluated for five different 8-bit
gray scales cover images in TIFF (Tagged Image File Format) image format.
TIFF images do not use compression and do not degrade in quality each time the
image is edited compared to JPEG images. Due to this exceptional feature of
TIFF format, it becomes prominent image type for the proposed scheme. Each
cover image used in this experiments need to be normalized into 256 x 256
pixels for watermark embedding purpose.
d) Watermark configuration
A watermark is generated using pseudo random sequences consisting 1
or 0 binary bits with the length of 128-bits.
4.3 Interim Experiment Results
LiREF watermarking is experimented with spatial domain of a cover image
according to algorithms described in Section 3.3.2. On the other hand, watermark
algorithms described in Section 3.3.3.2 and 3.3.3.3 have been experimented with level-
one and level-two wavelet domains separately. Embedding process works specifically at
robust edges of cover image, which are identified through edge detection technique in
spatial domain, and thresholding technique for wavelet domain. Inverse embedding
process is accomplished in both domains for watermark detection.
Five gray scale images have been used for the experiment as illustrated in Figure
4.1.
44
Figure 4.1 Cover images used for experiments
To investigate the robustness of the proposed algorithm against attacks, we
purposely attacked the watermarked image by scaling attack. This attack is caused by
MatLab program.
4.3.1 LiREF watermarking in spatial domain
This approach requires edge detection to be employed on spatial domain of
cover image, so that watermark is embedded on these robust edges. Figure 4.2 shows
the sample of edge detections for “Lena” image using Canny, Roberts, Prewitt and
Sobel operators. As seen in Figure 4.2, more edges can be spotted using Canny
followed by Sobel, Prewitt and Roberts edge detector.
(a) Baboon.tiff (b) Barbara.tiff
(e) Lena.tiff
(d) Peppers.tiff (c) Cameraman.tiff
45
Figure 4.2 Edge detections for Lena image
Figure 4.3 shows cover image of “Lena”, its watermarked copy, and difference
image. Roberts edge detection operator is used in edge detection process. Very little
differences can be notice along edges in the image.
Figure 4.3 Cover image and its watermarked copy
(a) Canny (b) Roberts
(c) Sobel (d) Prewitt
(a) Cover image (b) Watermarked Image (c) Difference image
46
4.3.2 LiREF watermarking in wavelet domain
LiREF watermarking scheme is experimented on both level-one and level-two
DWT domains separately. The Daubechies wavelet is used to produce the wavelet
coefficients. The motive is to test the robust coefficients levels to embed and extract the
watermark. Watermark is only embed in the three high pass sub bands denoted as
horizontal, diagonal, and vertical coefficients; when its value above thresholding value.
Figure 4.4 illustrate level-one 2-D DWT watermarking for “Lena” image, which
shows cover image of “Lena”, its watermarked copy, and difference image. The
selected threshold value is above 70.
Figure 4.4 Level-one 2-D DWT watermarking
Figure 4.5 shows level-two 2-D DWT watermarking for “Lena” image,
consisting of cover image of “Lena”, watermarked copy, and difference image. Selected
threshold value is above 70.
(a) Cover image (b) Watermarked Image (c) Difference image
47
Figure 4.5 Level-two 2-D DWT watermarking
4.4 Results Analysis
All the cover images are tested with LiREF watermarking scheme. Watermarked
image might be attacked either on purpose or accidentally, so the watermarking system
should able to detect and extract the watermark. To test the robustness of the proposed
algorithm against attacks, watermarked image is attacked by scaling distortion.
Finally, a percentage match, p between embedded watermark and detected
watermark is calculated based on equation (1), in Chapter 3, Section 3.3.2(b), for spatial
and wavelet domains.
4.4.1 LiREF watermarking in spatial domain
Table 4.1 lists results of LiREF watermarking experiments in spatial domain for
cover images after scaling attack, together with edge detection techniques, embedded,
detected watermark volume and p values.
(a) Cover image (b) Watermarked Image (c) Difference image
48
Table 4.1 LiREF watermarking experiment results in spatial domain
Based on the figures in Table 4.1, a line chart has been plotted to show the
comparison of p value among different cover images with related edge detection
operators. The line chart is shown in Figure 4.6.
Attack : Scaling (Original dimension > 256 x 256 > Original dimension)
Cover image Edge
detection operator
Embedded watermark bits /
volume
Extracted watermark bits /
volume Percentage match,
p
Baboon.tiff Canny 10645 5225 49
Sobel 2808 1310 47
Prewitt 2704 1233 46
Roberts 1292 662 51
Barbara.tiff Canny 5913 2927 50
Sobel 2954 1421 48
Prewitt 2909 1483 51
Roberts 2339 1181 50
Cameraman.tiff Canny 5747 3133 55
Sobel 2503 1346 54
Prewitt 2509 1544 62
Roberts 2341 1693 72
Lena.tiff Canny 5113 2475 48
Sobel 2573 1225 48
Prewitt 2523 1221 48
Roberts 2393 1179 49
Peppers.tiff Canny 4962 2454 49
Sobel 2581 1232 48
Prewitt 2567 1241 48
Roberts 2587 1324 51
49
0
10
20
30
40
50
60
70
80
90
100
Baboon.tiff Barbara.tiff Cameraman.tiff Lena.tiff Peppers.tiff
Cover Image
Per
cen
tage
mat
chCanny
Sobel
Prewitt
Roberts
Figure 4.6 Comparison of p value among cover images in spatial domain. As in the above Figure 4.6, among the cover images, “Cameraman” has the highest p value with all edge detection operators. The rest of images have highest p with Roberts edge detector, except for “Barbara” which shows highest p value with Prewitt edge detector.
4.4.2 LiREF watermarking in wavelet domain
a) Level-one 2-D DWT
Table 4.2 lists the embedded and detected watermark volume from
watermarked image in level-one 2-D DWT and their p values. Line charts
have been plotted based on the derived p value for H1, V1, D1 sub bands in
cover images together with selected threshold value, refer to Figure 4.7 to
Figure 4.11.
50
Table 4.2 LiREF watermarking experiment results in Level-one 2-D DWT
NA = Not Applicable. Detected watermark bits and percentage match, p becomes as NA, when there is no or zero embedded bits found in H1, D1, or V1 sub bands for a particular cover image at certain threshold value.
Attack : Scaling (Original dimension > 256 x 256 > Original dimension)
Level-one 2-D DWT
Embedded watermark bits /
volume Detected watermark
bits / volume Percentage match,
p
Cover image Threshold
value H1 V1 D1 H1 V1 D1 H1 V1 D1
>10 3485 3215 2018 1440 1318 341 41 41 17
>50 321 62 14 64 8 0 20 13 0
>70 110 8 0 30 1 NA 27 13 NA
>90 41 4 0 0 0 NA 0 0 NA
Baboon.tiff
>130 0 0 0 NA NA NA NA NA NA
>10 1130 2188 984 510 960 62 45 44 6
>50 56 64 0 6 9 NA 10 14 NA
>70 14 8 0 0 0 NA 0 0 NA
>90 0 0 0 NA NA NA NA NA NA
Barbara.tiff
>130 0 0 0 NA NA NA NA NA NA
>10 1509 1404 856 848 757 484 56 54 57
>50 186 305 38 94 195 38 51 64 100
>70 92 182 8 55 159 3 60 87 38
>90 46 102 2 32 52 2 70 51 100
Cameraman.tiff
>130 6 26 0 4 21 NA 67 81 NA
>10 990 1719 517 454 852 125 46 50 24
>50 47 155 0 18 43 NA 38 28 NA
>70 7 62 0 3 26 NA 43 42 NA
>90 1 23 0 0 7 NA 0 30 NA
Lena.tiff
>130 0 3 0 NA 0 NA NA 0 NA
>10 1108 1373 353 598 684 72 54 50 20
>50 66 180 1 28 90 0 42 50 0
>70 21 89 0 6 23 NA 29 26 NA
>90 8 33 0 1 8 NA 13 24 NA
Peppers.tiff
>130 0 1 0 NA 0 NA NA 0 NA
51
0
10
20
30
40
50
60
70
80
90
100
Baboon.tiff Barbara.tiff Cameraman.tiff Lena.tiff Peppers.tiffCover Image
Pe
rce
nta
ge
ma
tch
H1
V1
D1
Figure 4.7 p values for H1, V1, D1 sub bands of cover images; Threshold > 10. As in the above Figure 4.7, D1 sub band reveals lower p value for all cover images, except for “Cameraman”. H1 sub band shows highest p value for all cover images, except for “Lena”.
0
10
20
30
40
50
60
70
80
90
100
Baboon.tiff Barbara.tiff Cameraman.tiff Lena.tiff Peppers.tiff
Cover Image
Per
cen
tage
mat
ch
H1
V1
D1
NA(D1)
Figure 4.8 p values for H1, V1, D1 sub bands of cover images; Threshold > 50. As in the above Figure 4.8, “Cameraman” has the highest p value at D1 sub band. In contrast, “Baboon” and “Peppers” has zero p value followed by “Barbara” and “Lena” which reveal NA results at D1 sub band.
52
0
10
20
30
40
50
60
70
80
90
100
Baboon.tiff Barbara.tiff Cameraman.tiff Lena.tiff Peppers.tiff
Cover Image
Per
cen
tage
mat
chH1
V1
D1
NA(D1)
Figure 4.9 p values for H1, V1, D1 sub bands of cover images; Threshold > 70. As in the above Figure 4.9, “Barbara” shows zero p value for H1, V1 and NA for D1 sub bands. In further, “Baboon”, “Lena” and “Peppers” also reveal NA results for D1 sub band.
0
10
20
30
40
50
60
70
80
90
100
Baboon.tiff Barbara.tiff Cameraman.tiff Lena.tiff Peppers.tiff
Cover Image
Per
cen
tage
mat
ch
H1
V1
D1
NA(H1)
NA(V1)
NA(D1)
Figure 4.10 p values for H1, V1, D1 sub bands of cover images; Threshold > 90. As in the above Figure 4.10, “Barbara” shows NA results for H1, V1 and D1 sub bands. Detectable watermark is found in “Cameraman” at all sub bands, “Lena” at V1 sub band, and “Peppers” at H1 and V1.
53
0
10
20
30
40
50
60
70
80
90
100
Baboon.tiff Barbara.tiff Cameraman.tiff Lena.tiff Peppers.tiff
Cover Image
Per
cen
tage
mat
chH1
V1
D1
NA(H1)
NA(V1)
NA(D1)
Figure 4.11 p values for H1, V1, D1 sub bands of cover images; Threshold > 130. As in the above Figure 4.11, only “Cameraman” has revealed detectable watermark at H1 and V1 sub bands. “Lena” and “Peppers” have zero p value at V1 sub bands. The rest of the results are NA.
b) Level-two 2-D DWT
Table 4.3 lists the embedded and detected watermark volume from
watermarked image in level-two 2-D DWT and their p values. Line charts
have been plotted based on the derived p value for H2, V2, D2 sub bands in
cover images together with selected threshold value, refer to Figure 4.12 to
Figure 4.16.
54
Table 4.3 LIREF watermarking experiment results in Level-two 2-D DWT
Attack : Scaling (Original dimension > 256 x 256 > Original dimension)
Level-two 2-D DWT
Embedded watermark bits /
volume Detected watermark
bits / volume Percentage match,
p
Image Threshold H2 V2 D2 H2 V2 D2 H2 V2 D2
>10 1319 1398 1033 684 717 493 52 51 48
>50 288 193 86 122 91 33 42 47 38
>70 117 81 27 53 37 8 45 46 30
>90 26 34 9 7 12 0 27 35 0
Baboon
>130 4 4 0 2 2 NA 50 50 NA
>10 912 1044 480 481 539 222 53 52 46
>50 116 212 11 53 106 4 46 50 36
>70 72 104 2 37 43 1 51 41 50
>90 37 51 1 15 24 0 41 47 0
Barbara
>130 10 12 0 5 2 NA 50 17 NA
>10 687 565 479 385 299 249 56 53 52
>50 192 208 83 102 120 43 53 58 52
>70 116 167 45 56 94 20 48 56 44
>90 79 138 27 41 74 20 52 54 74
Cameraman.tiff
>130 47 97 5 24 43 2 51 44 40
>10 635 811 473 348 398 240 55 49 51
>50 124 302 60 60 141 16 48 47 27
>70 72 162 20 43 74 8 60 46 40
>90 35 108 5 21 45 2 60 42 40
Lena.tiff
>130 11 52 0 4 26 NA 36 50 NA
>10 854 789 405 433 389 214 51 49 53
>50 183 273 46 93 125 22 51 46 48
>70 104 196 15 52 96 5 50 49 33
>90 61 140 3 25 67 0 41 48 0
Peppers.tiff
>130 24 74 0 10 32 NA 42 43 NA
NA = Not Applicable. Detected watermark bits and percentage match, p becomes as NA, when there is no or zero embedded bits found in H2, D2, or V2 sub bands for a particular cover image at certain threshold value.
55
0
10
20
30
40
50
60
70
80
90
100
Baboon.tiff Barbara.tiff Cameraman.tiff Lena.tiff Peppers.tiff
Cover Image
Per
cen
tage
mat
ch
H2
V2
D2
Figure 4.12 p values for H2, V2, D2 sub bands of cover images; Threshold > 10. As in the above Figure 4.12, all cover images reveal consistent p values at varies sub bands. Meanwhile, “Barbara” has lowest p value at D2 sub band.
0
10
20
30
40
50
60
70
80
90
100
Baboon.tiff Barbara.tiff Cameraman.tiff Lena.tiff Peppers.tiff
Cover Image
Per
cen
tage
mat
ch
H2
V2
D2
Figure 4.13 p values for H2, V2, D2 sub bands of cover images; Threshold > 50. As in the above Figure 4.13, all cover images have very much consistent p value with the range 40%-60% at H2 and V2 sub bands. There are drastic differences of p value found at D2 sub band for all cover images except for “Cameraman”.
56
0
10
20
30
40
50
60
70
80
90
100
Baboon.tiff Barbara.tiff Cameraman.tiff Lena.tiff Peppers.tiff
Cover Image
Per
cen
tage
mat
ch
H2
V2
D2
Figure 4.14 p values for H2, V2, D2 sub bands of cover images; Threshold > 70. As in the above Figure 4.14, inconsistent p values are found for all cover images at all sub bands.
0
10
20
30
40
50
60
70
80
90
100
Baboon.tiff Barbara.tiff Cameraman.tiff Lena.tiff Peppers.tiff
Cover Image
Per
cen
tage
mat
ch
H2
V2
D2
Figure 4.15 p values for H2, V2, D2 sub bands of cover images; Threshold > 90. As in the above Figure 4.15, “Cameraman” has shown detectable watermark at this stage with the highest p value at D2 sub band and in contrast, zero p value are obtained for “Baboon”, “Barbara” and “Peppers” at t his sub band. Inconsistent p values are found at H2 and V2 sub bands.
57
0
10
20
30
40
50
60
70
80
90
100
Baboon.tiff Barbara.tiff Cameraman.tiff Lena.tiff Peppers.tiff
Cover Image
Per
cen
tage
mat
chH2
V2
D2
NA(D2)
Figure 4.16 p values for H2, V2, D2 sub bands of cover images; Threshold > 130 As in the above Figure 4.16, most of the cover images reveal NA results at D2 sub band except for “Cameraman”. H2 and V2 sub bands have varies performance of p value for all kind of cover images.
We compare our experiment results obtained in level-one and level-two 2-D
DWT as listed in Table 4.4 for threshold value above 70. A related column chart has
been tabulated based on figures in Table 4.4, refer to Figure 4.17.
Table 4.4 Comparison p values between Level-one and Level-two 2-D DWT Percentage Match, p
Threshold > 70
Level-one 2-D
DWT
Level-two 2-D
DWT
Image H1 V1 D1 H2 V2 D2
Baboon.tiff 27 13 NA 45 46 30
Barbara.tiff 0 0 NA 51 41 50
Cameraman.tiff 60 87 38 48 56 44
Lena.tiff 43 42 NA 60 46 40
Peppers.tiff 29 26 NA 50 49 33
NA = Not Applicable
58
Figure 4.17 Comparisons p values between Level-one and Level-two 2-D DWT. As in the above Figure 4.17, “Cameraman” reveals the highest p values in both level of 2-D DWT. This is followed by “Lena”, “Peppers”, “Baboon” and “Barbara” respectively.
4.5 Discussion
Based on the derived results from above, several ideas has been discussed as the
following. In spatial domain approach, more watermark volume are embedded in cover
images using Canny edge detection, since it able to reveal more robust edges compare
to other edge detectors (Senthil & Bhaskaran 2008), refer Table 4.1. The highest
watermark volume embedded in “Baboon” followed by “Barbara”, “Cameraman”,
“Lena”, and “Peppers”. Highest p value for overall cover images is shown with Roberts
edge detection, except for “Barbara”, where the highest p shown with Prewitt edge
detection. “Cameraman”, shows highest p since it was not affected by the scaling attack
as the rest of the images, refer to Figure 4.6. Hence, “Cameraman” is used as control
item for this experiment.
As referring to Table 4.2, level-one 2-D DWT watermarking reveals more
watermarks able to embed in lower threshold value. Seen from Figure 4.7 to 4.11, if the
H1
H1
H1
H1
H1
V1
V1
V1
V1
V1
D1 D1
D1
D1 D1
H2H2
H2
H2
H2V2
V2
V2
V2V2
D2
D2D2
D2D2
0
10
20
30
40
50
60
70
80
90
100
Baboon.tiff Barbara.tiff Cameraman.tiff Lena.tiff Peppers.tiff
Cover Image
Per
cent
age
mat
ch
H1
V1D1
H2
V2D2
* NA
* * * *
59
thresholding value is increased from 10 to 130, p values will drop from the range of
40-60% to 0%. This can be seen by analyzing the H1 sub band in “Pepper”, whereby
when the threshold changes from 70 to 50, the p values changed from 29% to 42%.
However when the thresholding increase to 130, it seems only single watermark bit
embedded in V1 sub band, and no watermark embedded in H1 and D1 sub bands.
Percentage match shown as 0% for V1 sub band and Not Applicable (NA) results for
H1 and D1 sub bands respectively.
As seen in Table 4.2, highest watermark volumes are embedded in V1 sub band
for “Barbara”, “Lena” and “Peppers”. Meanwhile for “Baboon” and “Cameraman”
highest watermark embedded in H1 sub band. Theoretically, watermark embedding
volume tend to be higher for H1 and V1 sub bands, since more edge information can be
seen at these sub bands (Reddy, Prasad, & Rao 2009). Furthermore, no watermark
embedded in D1 sub band when thresholding set above 70 for all images except for
“Cameraman”, which is embedded with 8 bits of watermark.
Table 4.3 lists the highest watermark embedded volume for all cover images
with thresholding above 10 in level-two 2-D DWT. But these numbers tend to decrease
when the thresholding is increased, refer Figure 4.13 to Figure 4.16. By comparing
results in Table 4.2 and Table 4.3, it is found that less watermark volume being
embedded in level-two compare to level-one. This is caused by the less number of
coefficients located in level-two sub bands. In contrast, higher p values found in level-
two, for instance, when thresholding is set above 70, p values for “Lena” are 60%, 46%
and 40% for H2, V2, D2 sub bands, meanwhile in level-one, p values are 43%, 42%
and NA for H1, V1 and D1 respectively, refer Figure 4.17. From Table 4.3, we
observed that p still can be found in H2 and V2 sub band even with thresholding above
60
130. On the other hand, lower watermark volume being embedded and detected in D2
sub band.
Through the observation of individual features of cover images, we conclude
some facts as the following. “Baboon” consists of more edge information on its fur,
which makes more watermarks embedded along these edges. “Barbara” represents the
second high textured regions found on its veil and pants. “Cameraman” which has high
contrast region between the shirt and background is not attacked by scaling distortion as
its dimension remains same throughout the watermarking process. Due to this factor,
“Cameraman” reveals highest p value among the rest images. “Lena” has smooth
background and textures on its hat, represents second lowest watermark embedded
volume. Lower watermarks have been embedded in “Peppers” since it has very smooth
content with less edge information.
4.6 Overall Analysis
In order to further evaluate the above experiments, the experiments’ results are
assessed based on Peak To Signal To Noise Ratio (PSNR) (Jun, Chi & Zhuang 2007;
Reddy, Prasad & Rao 2009) and execution time (Jun, Chi & Zhuang 2007). PSNR is
related to imperceptibility, which means it is used to indicate the changes in the
watermarked image and cover image. The PSNR is computed according to equation (2):
[ ]∑∑−
=
−
=
−•
=
•=1
0
21
010 ),('),(
1,
255log20
N
j
M
i
jiIjiINM
MSEdBMSE
PSNR …(2)
where MSE is the Mean Square Error of cover image and watermarked image.
M is the length (pixel) of the image and N is the width (pixel). 255 is gray level range of
image, I(i,j) and I’(i,j) are gray level values at pixel(i,j) of cover image and
watermarked image respectively. Execution time for a watermark algorithm refers to
61
actual CPU cycles time used in embedding watermark in cover image and watermark
detection.
Computed execution time, PSNR and MSE values are shown in Table 4.5, Table
4.6 and Table 4.7 for spatial domain with Roberts edge detection, level-one 2-D DWT
and level-two 2-D DWT respectively.
Table 4.5 Execution time, PSNR and MSE in spatial domain
Table 4.6 Execution time, PSNR and MSE in level-one 2-D DWT
Roberts edge detection
Embedding time
Detection time
PSNR MSE
Baboon.tiff 1.19 1.05 +31.05 dB 7.1461
Barbara.tiff 1.31 1.09 +33.27 dB 5.5349
Cameraman.tiff 1.31 0.98 +68.68 dB 0.0939
Lena.tiff 1.38 1.11 +38.03 dB 3.1983
Peppers.tiff 1.34 1.17 +37.49 dB 3.4031
Level-one 2-D DWT, Threshold > 70
Embedding time
Detection time
PSNR MSE
Baboon.tiff 1.19 0.52 +30.58 dB 7.5397
Barbara.tiff 1.19 0.50 +32.56 dB 6.0085
Cameraman.tiff 1.06 0.45 +36.85 dB 3.6633
Lena.tiff 1.27 0.56 +36.06 dB 4.0155
Peppers.tiff 1.20 0.55 +35.66 dB 4.2045
62
Table 4.7 Execution time, PSNR and MSE in level-two 2-D DWT
Higher PSNR in spatial domain is identified compare to wavelet domain, due to
differences in image distortions in both domains, as seen in Table 4.5 to 4.7.
Watermarked image in spatial domain only being distort by scaling attack but in
wavelet domain it has undergone three types of distortions; there are back and forth
image transformation between spatial and wavelet domain, back and forth
normalization of coefficients from real number into 8-bit integer format and scaling
attacks. These distortions have degraded the visual quality of watermarked image in
wavelet domain and weakens its robustness. The experiment result also shows PSNR in
level-two is lower compare to level-one due to the corruption of the cover image with
level-two image decomposition and scaling attack, refer to Figure 4.5. In contrast, level-
two is more reliable and robust compare to level-one, since level-two located at higher
level in wavelet domain and it is more generic in revealing the robust edges of an
image. This can be seen by referring to comparisons of p values among level-one and
level-two in Table 4.4. By observing results in Table 4.5 to 4.7 again, we found that
PSNR in all domains are above 30dB for scaling attacks, which means our proposed
watermarking scheme is still robust and could detect the watermark successfully with
good visual quality of watermarked image (Chemak 2008; Al-Khassaweneh 2007). In
Level-two 2-D DWT, Threshold > 70
Embedding time
Detection time
PSNR MSE
Baboon.tiff 1.31 0.45 +30.48 dB 7.6298
Barbara.tiff 1.23 0.56 +32.04 dB 6.3795
Cameraman.tiff 1.14 0.48 +36.43 dB 3.8477
Lena.tiff 1.30 0.52 +34.66 dB 4.7135
Peppers.tiff 1.34 0.56 +34.21 dB 4.9659
63
addition, the proposed scheme is robust to scaling, even when the watermarked image is
zoomed in into its original size.
It is also observed that wavelet domain reveals faster execution time compare to
spatial, with thresholding technique. Thresholding is a fast and simple implementation
for identifying robust edges in cover image compared to slower convolution approach
used by edge detection technique in spatial domain. Level-two 2-D DWT utilized more
CPU cycles times for embedding, since the embedder need to checks for the level-one
decomposition and level-two decomposition. In term of similarity, both levels in
wavelet domain have similar detection time. Overall embedding time of the embedder is
around 1 to 2 seconds and the detection time of the detector is around 0 to 2 seconds, as
seen in Table 4.5 to 4.7. This proves that our proposed scheme has low computational
complexity.
The proposed LiREF watermarking for level-two wavelet domain is compared
with previous research works (Lin et al. 2008; Wang, Chang & Pan 2006) for
robustness to scaling attack. Table 4.8 represents the PSNR values for recovered
watermark for several embedding schemes that have been scaling attack. Cover image
used is “Lena”. It is observed that our proposed scheme outperforms the other two
schemes against scaling attack. In addition, our scheme appears to be robust with simple
algorithm without the use of Fuzzy ART or SVD. Comparison for spatial domain is
omitted, since by theoretically it is not robust to geometrical attacks (Liu et al. 2005;
Jun, Chi & Zhuang 2007).
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Table 4.8 PSNR values of recovered watermark from embedding schemes
The robust performance of our proposed scheme lies on the fact of simple and
fast algorithm. This is because watermark data are placed on the robust detected edges
using edge detection and thresholding and do not require any additional variables and
their alteration to obtain optimum watermarked results. Moreover, the proposed scheme
reveals fast execution time in all domains. Due to simple and fast algorithm and
moderate PSNR values we conclude our proposed scheme to be robust lightweight
watermarking, whereby it is suitable to be implemented in real time environment
specifically for mobile platform.
4.7 Chapter Summary
Digital images can be easily distort with geometrical attacks and effects the
robustness features of image watermarking. In this dissertation, we developed a
watermarking scheme based on the LiREF for spatial and wavelet domain which is
robust to scaling attack. Watermark embedding strategy in spatial domain works by
extraction of robust edges in cover image using edge detection techniques and
watermark embedded along these edges. In wavelet domain, the watermark is
embedded into three detail high pass sub band of a cover image using thresholding,
which can guarantee the visual quality of the watermarked image. An inverse process of
watermark embedding for each domain is used separately for the purpose of watermark
Watermarking scheme PSNR [dB] of recovered watermark
A Robust Watermark Scheme for Copyright Protection (Lin et al. 2008)
32.632dB
A DWT-based Robust Watermarking Scheme with Fuzzy ART ( Wang, Chang & Pan 2006)
29.80 dB
The proposed approach +34.66 dB
65
detection. Both approaches have been experimented and proven to be robust to scaling
attack. This algorithm is very efficient in term of processing time.
66
Chapter 5: Conclusion
5.1 Summary of the Dissertation
Digital watermarking is a method of imperceptibly altering an original digital
content to embed a message about the content itself, which later can be used for the
purpose of copyright protection, fingerprinting, copy control, broadcast monitoring and
data authentication. Watermarking has better performance compared to cryptography,
since it is robust against varies image processing attacks. Moreover, watermarking
within digital image becomes significantly important nowadays.
In this dissertation research work, we have successfully implemented a new
lightweight robust watermarking using robust edge features in multiple domains
(LiREF). The proposed algorithm mainly focused on gray scale digital images. For
simplicity, a watermark is generated through pseudo random sequences and embedded
separately in spatial and wavelet domains along the robust edges of a cover image.
Those robust edges are distinguished using edge detection operator and thresholding
techniques for spatial and wavelet domain respectively. The embedded watermark is
successfully detected without the information of original cover image using an inverse
embedding process. Hence, this is known as blind watermark detection scheme. The
robustness of our proposed scheme is tested with resistance of watermarked image
against scaling attack.
67
5.2 Achievements
The objectives stated earlier in Chapter 1, have been achieved in order to
accomplish the dissertation goal which is to design and develop a lightweight robust
watermarking using robust edge features in multiple domains.
� A careful study and review of several literatures has been carried out
successfully in the area of robust watermarking and lightweight
watermarking for digital image. The findings are written in Chapter 2.
� A new algorithm has been analyzed and designed for lightweight robust
watermarking to improve weakness in current literatures. For watermark
embedding, we used edge detection operation with spatial and thresholding
technique with wavelet domain to discover robust edges in cover image. An
inverse process of watermark embedding for each domain is used separately
for the purpose of watermark detection. In order to test the robustness of
watermarked image against geometrical attack, we purposely distort the
watermarked image with scaling attack. The complete explanation is
included in Chapter 3.
� A new algorithm has been implemented, tested and evaluated in Chapter 4.
Through several experiments and analysis, it is proven that this algorithm is
a robust to scaling attack and very efficient in term of processing time.
Experimental results have demonstrated higher PSNR is gained in spatial
domain, due to less distortion to watermarked image. Meanwhile, wavelet domain
reveals lower computational complexity with thresholding method compared to slower
convolution approach used by edge detection technique in spatial domain. Even though,
level-two wavelet domain has lower PSNR, it shows more reliable p value compare to
68
level-one. Moreover, this proposed domain approach still shows better PSNR values of
distort watermarked image compared to (Lin et al. 2008; Wang, Chang, & Pan 2006).
Overall experiment observations, we noticed that the proposed scheme is simple
algorithm with low computational complexity and consists of moderate PSNR. These
are the three main factors contributed towards the robustness feature of our proposed
scheme. Therefore, this scheme turns out to be as robust lightweight watermarking and
it is suitable to be implemented in real time environment specifically for mobile
platform.
5.3 Future work
Our future work will be based on as the following:
� To apply spread spectrum approach in order to improve the
watermark detection rate.
� To experiment other geometrical attacks such as rotation and
translation and several image processing attacks against the
proposed idea.
� This proposed scheme is anticipated to be tested on mobile
platform and extended algorithm scheme for color images.
69
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