Multimedia Fingerprinting Forensics for Traitor Tracing: Pt. 4(EURASIP Book Series on Signal Processing & Communications)

The popularity of multimedia content has led to the widespread distribution and consumption of digital multimedia data. As a result of the relative ease with which individuals may now alter and repackage digital content, ensuring that media content is employed by authorized users for its intended purpose is becoming an issue of eminent importance to both governmental security and commercial applications. Digital fingerprinting is a class of multimedia forensic technologies to track and identify entities involved in the illegal manipulation and unauthorized usage of multimedia content, thereby protecting the sensitive nature of multimedia data as well as its commercial value after the content has been delivered to a recipient. "Multimedia Fingerprinting Forensics for Traitor Tracing" covers the essential aspects of research in this emerging technology, and explains the latest development in this field. It describes the framework of multimedia fingerprinting, discusses the challenges that may be faced when enforcing usage polices, and investigates the design of fingerprints that cope with new families of multiuser attacks that may be mounted against media fingerprints. The discussion provided in the book highlights challenging problems as well as future trends in this research field, providing readers with a broader view of the evolution of the young field of multimedia forensics. Topics and features include: comprehensive coverage of digital watermarking and fingerprinting in multimedia forensics for a number of media types; detailed discussion on challenges in multimedia fingerprinting and analysis of effective multiuser collusion attacks on digital fingerprinting; thorough investigation of fingerprint design and performance analysis for addressing different application concerns arising in multimedia fingerprinting; and, well-organized explanation of problems and solutions, such as order-statistics-based nonlinear collusion attacks, efficient detection and identification of colluders, group-oriented fingerprint design, and anti-collusion codes for multimedia fingerprinting. Presenting the state of the art in collusion-resistant digital fingerprinting for multimedia forensics, this invaluable book is accessible to a wide range of researchers and professionals in the fields of electrical engineering, computer science, information technologies, and digital rights management.

Table of Contents:
1. Introduction 1 2. Preliminaries on data embedding 7 2.1. Content protection via digital watermarking 7 2.1.1. Major applications and design requirements 8 2.1.2. Basic embedding approaches 9 2.2. Robust additive spread-spectrum embedding 11 2.2.1. Overview of spread-spectrum embedding 12 2.2.2. Distortion and attacks against robust embedding 13 2.2.3. Mathematical formulation 15 2.2.4. Alternative detection statistics 17 2.2.5. Exploiting human visual properties 21 2.3. Employing spread-spectrum embedding in fingerprinting 23 3. Collusion attacks 25 3.1. Introduction to collusion attacks 26 3.1.1. Linear collusion attacks 26 3.1.2. Nonlinear collusion attacks 28 3.2. Introduction to order statistics 29 3.2.1. Distribution of order statistics 30 3.2.2. Joint distribution of two di.erent order statistics 30 3.2.3. Joint distribution of order statistics and the unordered random variables 31 3.3. Multimedia fingerprinting system model 33 3.3.1. Fingerprinting systems and collusion attacks 33 3.3.2. Performance criteria 35 3.4. Statistical analysis of collusion attacks 36 3.4.1. Analysis of collusion attacks 36 3.4.2. Analysis of detection statistics 41 3.4.3. System performance analysis 42 3.5. Collusion attacks on Gaussian-based fingerprints 43 3.5.1. Unbounded Gaussian fingerprints 43 3.5.2. Bounded Gaussian-like fingerprints 48 3.6. Preprocessing of the extracted fingerprints 52 3.7. Experiments with images 57 3.8. Chapter summary 61 4. Orthogonal fingerprinting and collusion resistance 63 4.1. Collusion resistance analysis 65 4.1.1. The maximum detector 66 4.1.2. The thresholding detector 68 4.2. Extensions to other performance criteria 78 4.3. Extensions to other types of attacks 83 4.4. A practical estimator for the amount of colluders 88 4.5. Experiments with images 90 4.6. E.cient fingerprint detection using tree structure 94 4.6.1. Tree-structured detection strategy 94 4.6.2. Experiments on tree-based detector 98 4.7. Chapter summary 99 5. Group-oriented fingerprinting 101 5.1. Motivation for group-based fingerprinting 102 5.2. Two-tier group-oriented fingerprinting system 105 5.2.1. Fingerprint design scheme 105 5.2.2. Detection scheme 106 5.2.3. Performance analysis 111 5.3. Tree-structure-based fingerprinting system 121 5.3.1. Fingerprint design scheme 121 5.3.2. Detection scheme 122 5.3.3. Parameter settings and performance analysis 124 5.4. Experimental results on images 132 5.5. Chapter summary 135 6. Anticollusion-coded (ACC) fingerprinting 137 6.1. Prior work on collusion-resistant fingerprinting for generic data 139 6.2. Code modulation with spread-spectrum embedding 142 6.3. Combinatorial designs 143 6.4. Combinatorial-design-based anticollusion codes 148 6.4.1. Formulation and construction of ACC codes 149 6.4.2. Examples of BIBD-based ACC 150 6.4.3. ACC coding e.ciency and BIBD design methods 152 6.5. Detection strategies and performance tradeo.s 154 6.5.1. Hard detection 156 6.5.2. Adaptive sorting approach 157 6.5.3. Sequential algorithm 157 6.6. Experimental results for ACC fingerprinting 158 6.6.1. ACC simulations with Gaussian signals 158 6.6.2. ACC experiments with images 163 6.7. A unified formulation on fingerprinting strategies 164 6.8. Chapter summary 168 7. Secure fingerprint multicast for video streaming 171 7.1. Secure video streaming 172 7.2. Prior art in secure fingerprint multicast 173 7.3. General fingerprint multicast distribution scheme 174 7.4. Joint fingerprint design and distribution scheme 176 7.4.1. Comparison of fingerprint modulation schemes 177 7.4.2. Joint fingerprint design and distribution 180 7.4.3. Addressing the computation constraints 185 7.5. Analysis of bandwidth e.ciency 186 7.5.1. "Multicast only" scenario 186 7.5.2. General fingerprint multicast scheme 187 7.5.3. Joint fingerprint design and distribution scheme 191 7.6. Robustness of the embedded fingerprints 194 7.6.1. Digital fingerprinting system model 194 7.6.2. Performance criteria 195 7.6.3. Comparison of collusion resistance 195 7.7. Fingerprint drift compensation 199 7.8. Chapter summary 202 8. Fingerprinting curves 205 8.1. Introduction 205 8.2. Basic embedding and detection 208 8.2.1. Feature extraction 208 8.2.2. Fingerprinting in the control-point domain 210 8.2.3. Fidelity and robustness considerations 212 8.2.4. Experiments with simple curves 215 8.3. Iterative alignment-minimization algorithm for robust fingerprint detection 219 8.3.1. Problem formulation 221 8.3.2. Iterative alignment-minimization algorithm 222 8.3.3. Detection example and discussion 225 8.4. Experiments with maps 228 8.5. Chapter summary 237 Bibliography 239 Index 251

About the Author :
K. J. Ray Liu is an associate professor in the Electrical Engineering Department and Institute for Systems Research at the University of Maryland in College Park. Dr. Liu's research interests span all aspects of signal processing with application to image/video, wireless communications, networking, and medical biomedical technology. He has published more than one hundred papers, many of which are in archival journals, books, and book chapters. He has won many awards, including the IEEE Signal Processing Society's Senior Award for Best Paper in VLSI in 1993 and the National Science Foundation Young Investigator Award in 1994. Wade Trappe received his B.A. degree in Mathematics from The University of Texas at Austin in 1994, and the Ph.D. in Applied Mathematics and Scientific Computing from the University of Maryland in 2002. He is currently an assistant professor at the Wireless Information Network Laboratory (WINLAB) and the Electrical and Computer Engineering Department at Rutgers University. His research interests include multimedia security, cryptography, wireless network security, and computer networking. Dr. Trappe is a co-author of the textbook "Introduction to Cryptography with Coding Theory", Prentice Hall, 2001. He is a member of the IEEE Signal Processing, Communication, and Computer societies. Z. Jane Wang is an Assistant Professor in the Electrical and Computer Engineering Department, University of British Columbia, Canada. She received the B.Sc. degree from Tsinghua University, China, in 1996 (with the highest honor), and the M.Sc. and Ph.D. degrees from the University of Connecticut in 2000 and 2002 (with the Outstanding Engineering Doctoral Student Award), respectively, all in electrical engineering. Since Aug. 2004, she has been an Assistant Professor in the Department of Electrical and Computer Engineering at University of British Columbia. Dr. Wang's research interests are in the broad areas of statistical signal processing, information security, wireless communications and genomic signal processing and statistics. Min Wu received the B.E. degree in electrical engineering and the B.A. degree in economics from Tsinghua University, Beijing, China, in 1996 (both with the highest honors), and the M.A. degree and Ph.D. degree in electrical engineering from Princeton University in 1998 and 2001, respectively. Since 2001, she has been an Assistant Professor of the Department of Electrical and Computer Engineering and the Institute of Advanced Computer Studies at the University of Maryland, College Park. Dr. Wu's research interests include information forensics and security, multimedia signal processing, and multimedia communications. She co-authored a book "Multimedia Data Hiding" (Springer-Verlag, 2003) and holds four U.S. patents. She is a member of the IEEE Technical Committees on Multimedia Signal Processing and on Multimedia Systems and Applications. She served as a guest editor of the Special Issue on Media Security and Rights Management for the EURASIP Journal on Applied Signal Processing. Hong Zhao received his B.S, M.S. and Ph.D degrees all in Chemical Engineering from Zhejiang University, China. Since then he has been working in Process Modeling and control R&D for more than 20 years. He had been a visiting associate professor in Technical University of Denmark from 1991-1993, University of Maryland from 1994-1996. He co-authored two books and published more than 20 papers. He holds a US patent and has been working on the development of many Aspen Tech APC products. With Aspen Tech, he developed the Subspace Identification technology, PID Watch product for process monitoring and PID-loop tuning. Currently he is a Senior Technologist of Aspen Technology, Inc.