Digital watermarking, steganography, and forensics are important topics because digital multimedia are widely used and the Internet is rapidly growing. This book intends to provide a comprehensive overview of the different aspects of mechanisms and techniques for information security. It has been written for students, researchers, and professionals who take related courses, want to improve their knowledge, and want to gain experience pertaining to the role of digital watermarking, steganography, and forensics.
Digital watermarking technology can be used to guarantee authenticity and can be applied as proof that the content has not been altered since insertion.
Steganographic messages are often first encrypted by some traditional means, and then a covert text is modified in some way to contain the encrypted message.
Digital forensics utilizes computational techniques to process multimedia content and then applies the results to help crime investigations. The need for information security exists everywhere every day.
This book aims to provide students, researchers, and professionals with technical information regarding digital watermarking, steganography, and forensics, as well as instruct them on the fundamental theoretical framework in developing the extensive advanced techniques. By comprehensively considering the essential principles of digital watermarking, steganography, and forensics systems, one can not only obtain novel ideas in implementing the advanced algorithms, but can also discover the new problems. The principles of digital watermarking, steganography, and forensics in this book are illustrated with plentiful graphs and examples in order to simplify the problems, so readers can easily understand even complicated theories. Several robust algorithms that are presented in this book to illustrate the framework provide assistance and tools in understanding and implementing the fundamental principles.
OVERVIEW OF THE SBOOK
The book is divided into four parts: Multimedia Mining and Classification (Chapters 1, 2, 3), Watermarking (Chapters 4, 5, 6, 7, 8, 9, 10), Steganography (Chapters 11, 12, 13, 14), and Forensics (Chapters 15, 16, 17, 18, 19, 20). In Chapter 1, the feasibility, techniques, and demonstrations of discovering hidden knowledge by applying multimedia duplicate mining methods to the massive multimedia content are introduced. Three promising knowledge-discovery applications are demonstrated to show the benefits of duplicate mining. In Chapter 2, a new framework is proposed for video concept classification with the help of discriminative learning and multiple correspondence analysis. In Chapter 3, an improved bag of feature and feature vocabulary-based technique of replacing the Harris-affine detection method by a random sampling procedure together with an increased number of sample points is presented. It is shown that the method improves categorization accuracy on a five-category problem using the Caltech-4 dataset.
Chapters 4, 5, 6, 7, 8, 9, 10 address digital watermarking techniques. In Chapter 4, the problems of detecting visible watermarks from images and removing the watermarks to generate watermark-free images are described. A new Fourier-based image alignment method with iterative refinement for the watermark detection is presented. In Chapter 5, a digital watermarking technique based on a chaotic map and a reference register is presented. A block-based chaotic map, which outperforms the traditional one by breaking local spatial similarity, is used to increase the amount of significant coefficients in the transformed image. A reference register is then employed to locate specific coefficients of a container efficiently for watermark embedding and extraction. In Chapter 6, a pseudo-random pixel rearrangement algorithm to improve the security of most image watermarking techniques is introduced. It rearranges image pixels based on the properties of Gaussian integers and results in a more random-looking image transformation that, in turn, significantly improves the security of the embedded watermark. In Chapter 7, an overview of existing fragile reversible data-hiding schemes is provided. Basic schemes are introduced and examples are illustrated to explain some complicated schemes. In Chapter 8, a novel semi-fragile spatial watermarking method based on local binary pattern (LBP) operators by using local pixel contrast for the embedding and extraction of watermarks is presented. A general framework for multilevel image watermarking is also extended. In Chapter 9, an efficient authentication method for JPEG images based on genetic algorithms (GA) is presented. A two-level detection strategy is also introduced to reduce the false acceptance ratio of invalid blocks. In Chapter 10, an efficient block-based fragile watermarking system for tamper localization and recovery of images is proposed. The cyclic redundancy checksum (CRC) is used to authenticate the feature of a block stored in a pair of mapping blocks. The proposed scheme localizes the tampered area irrespective of the gray value, and the detection strategy is designed in such a way to reduce false alarms.
Chapters 11, 12, 13, 14 cover digital steganography techniques. Chapter 11 surveys image steganography and steganalysis. It introduces the key concepts behind image steganography and steganalysis, the history and origin of steganography, and the steganography and steganalysis tools currently available. In Chapter 12, different types of image steganographic schemes based on vector quantization (VQ) are presented. The concept of VQ is introduced first, and the main components, such as the input image to be compressed, the VQ codebook, the VQ indices of input image, and the reconstructed image, are applied to digital steganography. In Chapter 13, a different evolutionary approach, named differential evolution (DE), is used to increase the performance of the steganographic system. The key element that distinguishes DE from other population-based approaches is differential mutation, which aims to find the global optimum of a multidimensional, multimodal function. In Chapter 14, a robust steganographic system is presented by artificially counterfeiting statistic features instead of the traditional strategy by avoiding the change of statistic features. A GA-based methodology by adjusting gray values of a cover-image while creating the desired statistic features to generate the stego-images that can break the inspection of steganalytic systems is described.
Chapters 15, 16, 17, 18, 19, 20 discuss digital forensic techniques. In Chapter 15, a robust image inpainting algorithm to determine a new filling order based on the structure priority value is introduced. The algorithm can fill the missing area with more accurate structure information. In addition, the dynamic searching range is used to improve the efficiency of finding the best patches in the source region. In Chapter 16, the techniques of copy–cover image forgery are discussed and four detection methods for copy–cover forgery detection, which are based on PCA, DCT, spatial domain, and statistical domain, are compared. Their effectiveness and sensitivity under the influences of Gaussian blurring and lossy JPEG compressions are investigated. In Chapter 17, an algorithm for both modification detection and localization, whose structure can support parallel-processing mode, is proposed. The mechanism of both changeable-parameter and self-synchronization is used to achieve all the performance requirements of hash function. In Chapter 18, video forensics is discussed, in which a major proportion of related research work is to perform mining for criminal evidence in videos recorded by a heterogeneous collection of surveillance camcorders. This is a new interdisciplinary field, and people working in the field need video-processing skills as well as an in-depth knowledge of forensic science; hence, the barrier for entering the field is high. In Chapter 19, the problems of vulnerability in the original multi-chaotic systems–based image encryption scheme are analyzed. A self-synchronizing method is proposed as an enhancement measure to solve the problems and defeat cryptanalysis. In Chapter 20, recent advances on human motion behavior modeling, including abilities to detect changes in human motion behavior, and to classify the types of small group motion behavior, are presented. Experimental results on several public human action datasets are provided to demonstrate the effectiveness of the presented methods in addressing these human behavior modeling challenges.
FEATURES OF THE BOOK
• New state-of-the-art techniques for digital watermarking, steganography, and forensics
• Numerous practical examples
• A more intuitive development to the complex technology
• Updated bibliography
• Extensive discussion on watermarking, steganography, and forensics
• Inclusion of watermarking, steganalysis, and forensics techniques and their counter-examples
FEEDBACK ON THE BOOK
It is my hope that an opportunity is given to correct any error in this book; therefore, please provide a clear description of any error that you may find.
Your suggestions on how to improve the textbook are always welcome. For this, use either email ([email protected]) or regular mail to the author:
Dr. Frank Y. Shih
College of Computing Sciences
New Jersey Institute of Technology
University Heights
Newark, NJ 07102-1982