Multimedia Security: Watermarking, Steganography, and Forensics outlines essential principles, technical information, and expert insights on multimedia security technology used to prove that content is authentic and has not been altered. Illustrating the need for improved content security as the Internet and digital multimedia applications rapidly evolve, this book presents a wealth of everyday protection application examples in fields including multimedia mining and classification, digital watermarking, steganography, and digital forensics.
Giving readers an in-depth overview of different aspects of information security mechanisms and methods, this resource also serves as an instructional tool on how to use the fundamental theoretical framework required for the development of extensive advanced techniques. The presentation of several robust algorithms illustrates this framework, helping readers to quickly master and apply fundamental principles.
Presented case studies cover:
Useful for students, researchers, and professionals, this book consists of a variety of technical tutorials that offer an abundance of graphs and examples to powerfully convey the principles of multimedia security and steganography. Imparting the extensive experience of the contributors, this approach simplifies problems, helping readers more easily understand even the most complicated theories. It also enables them to uncover novel concepts involved in the implementation of algorithms, which can lead to the discovery of new problems and new means of solving them.
Part I: Multimedia Mining and Classification
Multimedia Duplicate Mining toward Knowledge Discovery, X. Wu, S. Poullot, and S. Satoh
Discriminative Learning-Assisted Video Semantic Concept Classification, Q. Zhu, M.-L. Shyu, and S. Chen
An Improved Feature Vocabulary-Based Method for Image Categorization, F.Y. Shih and A. Sheppard
Part II: Watermarking
Automatic Detection and Removal of Visible Image Watermarks, H. Su, Y. Cheng, and S. Lai
Digital Watermarking Based on Chaotic Map and Reference Register, Y.-T. Wu and F.Y. Shih
A Pseudo-Random Pixel Rearrangement Algorithm Based on Gaussian Integers for Image Watermarking, A. Koval, F.Y. Shih, and B.S. Verkhovsky
Reversible Data-Hiding Techniques for Digital Images, Z.-H. Wang, M.-T. Sun, and C.-C. Chang
Watermarking Based on Local Binary Pattern Operators, W. Zhang and F.Y. Shih
Authentication of JPEG Images Based on Genetic Algorithms, V. Gopal Edupuganti and F.Y. Shih
An Efficient Block-Based Fragile Watermarking System for Tamper Localization and Recovery, V. Gopal Edupuganti, F.Y. Shih, and I-C. Chang
Part III: Steganography
Survey of Image Steganography and Steganalysis, M. Bachrach and F.Y. Shih
Digital Steganographic Schemes Based on Image Vector Quantization, S.D. Lin and S.-C. Shie
A Differential Evolution-Based Algorithm for Breaking the Visual Steganalytic System, F.Y. Shih and V. Gopal Edupuganti
Genetic Algorithm-Based Methodology for Breaking the Steganalytic Systems, Y.-T. Wu and F.Y. Shih
Part IV: Forensics
Image Inpainting Using an Enhanced Exemplar-Based Algorithm,I-C. Chang and C.-W. Hsu
A Comparison Study on Copy-Cover Image Forgery Detection, F.Y. Shih and Y. Yuan
A Chaos-Based Hash Function with Both Modification Detection and Localization Capabilities, D. Xiao, F.Y. Shih, and X. Liao
Video Forensics, H.-R. Tyan and H.-Y. Mark Liao
Using the Self-Synchronizing Method to Improve Security of the Multi-Chaotic Systems-Based Image Encryption, D. Xiao and F.Y. Shih
Behavior Modeling of Human Objects in Multimedia Content, Y. Yin and H. Man
Internationally renowned scholar Frank Y. Shih received his BS degree from the National Cheng Kung University, Taiwan, in 1980. He earned an MS degree from the State University of New York, Stony Brook, USA, in 1983, and a Ph.D from Purdue University, West Lafayette, Indiana, USA, in 1987. He is currently a professor at New Jersey Institute of Technology, Newark, jointly appointed in the departments of Computer Science, Electrical and Computer Engineering, and Biomedical Engineering. He is also director of the school’s Computer Vision Laboratory.