This function creates a suitable watermark according to the desired applications. In simple applications, the watermark can be a text, logo or binary code. In the sophisticated applications, the watermark may have particular properties based on the desired objectives. For example, in medical application, the watermark may need to combine the patient information or some features of medical data to ensure the identification, authentication and integrity of the watermarked data.
The watermark embedding process is achieved at the sender side. In this step, the watermark is added to the original data (image, audio and video) by applying a certain algorithm and using a secret key. The result of watermark embedding process is a watermarked data.
The watermark extraction process is achieved at the receiver side. In this step, the reverse implementation of watermark embedding algorithm is applied to ex-tract the embedded watermark from watermarked data or to identify whether any other watermark is embedded in the data. The watermark extraction algo-rithm use the secret key and/or the original data to detect/extract the embedded watermark.
Data type based categorizations
Text, image, audio or video watermarking refers to embedding watermarks in text/image/audio/video in order to protect the data content from copying, trans-mitted or manipulated anonymously. In text watermark, the varying spaces after punctuation, spaces between lines and the spaces at the end of sentences could be significant features used to gener-ate the watermark or to find proper locations in text for embedding watermark. In audio, image and video watermarking, the watermark could be embedded in the low/high frequency coefficients of frequency domain or could be embedded directly in the least significant bits of spatial data.
Human perception based categorizations
Based on human perception property, digital watermarking approaches are clas-sified into three categories: visible, invisible and dual approaches. In visible wa-termarking, the watermark is inserted into the original data in such a way that it is visible to the human eye. Visible watermark is used to indicate the ownership of multimedia data. The logo or seal of the organizations, which are stamped on the documents, images, video or TV channels for content and ownership identi-fication are the most popular examples of visible watermarks.
In invisible watermarking, the watermark is inserted into the original data in such away that is intended to be imperceptible to the human eye. Invisible watermark can be detected only though watermark extraction algorithm and is suitable for many purposes including: ownership identification, authentication and integrity verification.
In some applications, visible and invisible watermarks can be applied together. This procedures is called the dual watermarking, and in this situation, the invis-ible watermark is assumed as a backup for the visible one.
Robustness based categorizations
Based on the robustness of digital watermarking, the invisible watermarking approaches can be divided into four categories: robust, fragile, semi-fragile and hybrid techniques. Robust watermarking approaches are intended to survive various manipula-tions on data content via unauthorized removal, unauthorized embedding and unauthorized detection attacks as well as to fulfill their expected purpose. Ro-bust approaches are typically used to detect misappropriated data for data au-thentication and integrity.
In fragile watermarking approaches, the watermark is intolerant to slight mod-ifications. This approaches are usually used to verify the integrity of data. The tamper proof is one benefit of fragile watermarking; losing watermark implies tampering occurred.
The semi-fragile watermarking approaches achieve moderate robustness against designated class of attacks. In these approaches, the watermark resist uninten-tional modifications, but it fails after intentional malicious modifications. This kind of approaches can be used to verify the reliability (authentication or in-tegrity) of data content. Some watermarking approaches may combine the fragile and robust methods to achieve the authenticity, integrity and ownership protec-tion simultaneously. Generally, invisible robust watermarks are used to detect misappropriated data, data authentication such as evidence of ownership, while the invisible frag-ile watermarks are used to verify the integrity of data content.
Extraction based categorizations
The digital watermarking approaches, based on extraction techniques, can be classified into three categories: blind, semi-blind and non-blind watermarking. The blind watermarking approaches need only robust key to extract the water-mark from the attacked watermarked data. These approaches are known as pub-lic approaches, since they use a public key in the extraction process. Comparing with other types of watermarking approaches, the blind approaches require less information storage at receiver side. The source end will send only the public key and the watermarked data. The semi-blind watermarking approaches require the original watermark and the key to extract the embedded watermark from the watermarked data. These approaches are known as semi-private approaches, because the original water-mark is shared between the sender and the receiver.
The non-blind watermarking approaches require the original watermark, the key and the original data to extract the embedded watermark from watermarked data. These approaches are known as private approaches, where the watermark is usually generated from the original data itself. This kind of watermarking is more preferable for tamper-proof application.
Reversibility based classification
The reversibility is an important requirement for some applications that deal with sensitive digital data such as medical, military and law-enforcement appli-cations. The reversible watermarking approaches guarantee extraction of both the embedded watermark and the original data exactly from the watermarked data. For tele-diagnosis purpose, the medical data should not be altered and for decision making purposes the military and law-enforcement data should not be changed. The reversibility requirement is met in lossless scheme of digital watermarking. In contrast, the irreversibility refers to extract the embedded watermark and the original data from watermarked data but not exactly as to the original ones. The irreversibility requirement is met in lossy scheme of digital watermarking. The lossless and lossy schemes of digital watermarking are discussed below.
Table of contents :
1 digital image processing fundamentals
1.2 Conception of Digital Image
1.3 Digital Image Representation
1.4 Digital Image Characteristics
1.5 Intelligent Methods and Techniques in Digital Image Processing .
1.6 Digital Image Processing Tools
2 digital image watermarking
2.2 Motivations for Digital Watermarking
2.3 Digital Watermarking Requirements
2.4 Digital Watermarking Framework
2.4.1 Watermark generation
2.4.2 Watermark embedding
2.4.3 Watermark extraction
2.5 Digital Watermarking Classification
2.5.1 Data type based categorizations
2.5.2 Human perception based categorizations
2.5.3 Robustness based categorizations
2.5.4 Extraction based categorizations
2.5.5 Reversibility based classification
2.6 Digital Image Watermarking Techniques
2.6.1 Spatial domain techniques
2.6.2 Transform domain techniques
2.6.3 Spread-spectrum domain
2.7 Attacks on Digital Images
2.7.1 Removal Attacks
2.7.2 Geometric Attacks
2.7.3 Property Attacks
2.7.4 Cryptographic Attacks
2.8 Digital Image Watermarking Performance Metrics
2.8.3 Embedding Rate Measures
2.9 Digital Image Watermarking Benchmark
3 literature reviews
3.2 Zero-Watermarking Based Approaches
3.3 ImageWatermarking Approaches Using Spatial Pixels/Transformed Coefficients
3.3.1 Medical Image Watermarking Approaches
3.3.2 Human Visual System Based ImageWatermarking Approaches
3.3.3 Intelligent Techniques and Human Visual System Based Image Watermarking Approaches
ii contribution 83
4 zero-watermarking approach for medical images based
on jacobian matrix 85
4.2 Jacobian Matrix
4.3 Proposed Zero-Watermarking approach
4.3.1 Extracting the quantization matrix from JPEG Bitstream .
4.3.2 Key (k) Extraction
4.3.3 Sending Process
4.3.4 Receiving Process
4.4 Experiment Results
4.4.1 Robustness results
4.4.2 Execution Time
4.5 Computational complexity analysis
4.6 Comparative Study
4.7 System Analysis
4.7.1 Selecting the Key k
4.7.2 Using the Jacobian Matrix
4.7.3 Security Requirement
4.7.6 Computational Complexity and Execution Time
5 image watermarking approach based on rough set theory
5.2 Classical Set and Rough Set Principles
5.3 Watermarking Approach in Spatial Domain based on HVS characteristics and Rough Set Theory
5.3.1 Problem statement
5.3.2 System model
5.3.4 Construction of an Information System for Digital Images .
5.3.5 Rough Set Implementation
5.3.6 Embedding Process
5.3.7 Extraction Process
5.4 Experiment Results
5.4.1 Watermark imperceptibility
5.4.2 Watermarking robustness
5.4.3 Embedding rate analysis
5.4.4 Execution time result
5.5 Computational complexity analysis
5.6 Comparative Study
5.6.1 Comparing the imperceptibility results
5.6.2 Comparing the robustness results
5.7 System Analysis
5.7.1 Using rough set theory
5.7.2 Imperceptibility and robustness
5.7.3 Computational complexity and execution time
5.7.4 Embedding rate
6 image watermarking approaches based on texture analysis
6.2 Problem Statement
6.3 Texture Analysis of digital images
6.3.1 DC coefficient
6.4 Image Watermarking Approaches Based on Texture Analysis Using Multi-Criteria Decision Making
6.4.1 Multi-Criteria Decision Making Problem
6.4.2 Proposed Approaches
6.4.3 Experiment Results
6.4.4 Computational complexity
6.5 Image Watermarking Approach Based on Texture Analysis Using Formal Concept Analysis
6.5.1 Principle of Formal Concept Analysis
6.5.2 Proposed Approach
6.5.3 Experiment Results
6.5.4 Computational complexity
6.6 Image Watermarking Approach Based on Texture Analysis and Using Frequent Pattern Mining
6.6.1 Principle of Frequent Patterns Mining
6.6.2 Principle of Apriori Algorithm
6.6.3 Proposed Approach
6.6.4 Experiment Results
6.6.5 Computational complexity
6.7 Image Watermarking Approach Based on Texture Analysis Using Association Rule Mining
6.7.1 Image mining and association rules
6.7.2 Mining process metrics
6.7.3 Proposed approach
6.7.4 Experiment Results
6.7.5 Computational complexity
6.8 Comparative Study
6.8.1 Comparing the imperceptibility results
6.8.2 Comparing the robustness results
6.9 System Analysis
7.1 Contribution Summary
7.1.1 Zero-watermarking approach for medical images based on Jacobian matrix
7.1.2 Spatial domain based image watermarking
7.2 Future Work
iv résumé en français