Seminars

• (Upcoming) Biometric Recognition: A New Paradigm for Security
  Speaker: Prof. Anil K. Jain, Michigan State University
  Friday, Jan. 25, 2008, 11:00 am - 12:00 pm
  Room: W1004, Lafferre Hall

Abstract: Can Alice be allowed to enter the country? Is Bob entitled to access this privileged information? Are we administering our service exclusively to the enrolled users? Is Charlie the real owner of this credit card? Every day, a variety of organizations pose questions such as these about the identity of individuals. Identity theft has become a far too easy crime these days and it is estimated that in the United States alone, individuals and businesses have suffered losses to the tune of $56.6 billion due to the problem of identity theft. An emerging technology that is becoming more widespread in tackling identity theft is biometrics - automatic personal recognition based on anatomical or behavioral characteristics such as face fingerprint, voice and signature. Biometrics allows us to confirm or establish an individual¡¯s identity based on who he is, rather than by what he possesses (e.g., an ID card) or what he remembers (e.g., a password). Biometric systems also introduce an aspect of user convenience; they alleviate the need for a user to remember multiple passwords associated with different applications or carry multiple ID cards. Biometric systems can provide higher security and minimize financial fraud compared to traditional authenticators. However, a practical biometric system must meet accuracy and speed requirements, satisfy resource constraints, be non-invasive and acceptable to the target population, and demonstrate robustness to various fraudulent methods and attacks. In this talk we will present an overview of biometric recognition, its advantages and limitations, and the challenges in dealing with accuracy, individuality, fusion and security issues.

Biography: Anil Jain is a University Distinguished Professor in the Department of Computer Science at Michigan State University. His research interests include pattern recognition, computer vision and biometric authentication. He has received Guggenheim, Humboldt, Fulbright and IEEE Computer Society Technical Achievement awards. He is a fellow of ACM and IEEE and served as the Editor-in-Chief of the IEEE Transactions on Pattern Analysis and Machine Intelligence. He holds six patents in fingerprint matching and is the co-author of following books: Handbook of Biometrics (2007), Handbook of Multibiometrics (2006), Handbook of Face Recognition (2005), Handbook of Fingerprint Recognition (2003) and Algorithms for Clustering Data (1988). He is a member of the Biometrics Defense Support Team and serves on The National Academies committees on Whither Biometrics and Improvised Explosive Devices.

• Optimum Detection for Perceptual Spread-Spectrum Watermarking
  Speaker: Wei Liu, UMC
  Tuesday, Oct. 2, 2007, 4-5 pm, 222 EBW
  

Abstract: Digital watermarking is an efficient and promising approach to protect intellectual property rights of digital media. Spread spectrum (SS) is one of the most widely used image watermarking schemes because of its robustness against attacks and its support for the exploitation of the properties of the human visual system (HVS). To maximize the watermark strength without introducing visual artifacts, in SS watermarking, the watermark signal is usually modulated by the so-called just-noticeable difference (JND) of the host image. In advanced perceptual models, the JND is characterized as a non-linear function of local image features. The optimum detection scheme for such non-linearly embedded watermarks, however, has rarely been studied. In this talk, we address this problem by deriving an optimum generalized correlation detector (GCD). The performance of a GCD is analyzed and the optimum GCD is solved according to the Neyman-Pearson criterion. We also show that the locally optimum detector (LOD) is always in the form of GCD, thus the optimality of our solution is confirmed. By using the proposed detector, we are able to deal with arbitrary host signal distributions and arbitrary JND models that exploit the self-masking property of the HVS. Simulation results demonstrate the superior performances of the proposed detector over the conventional linear correlation detector (LCD).

Biography: Wei Liu received his B.E. and M.E. degrees from Tsinghua University, China, in 1999 and 2002, respectively, both in electrical engineering. He is now pursuing the Ph.D. degree in the Computer Science Department, University of Missouri-Columbia, USA, and is currently a fellow of the Center for Cyber Security Research. He was the winner of the 2007 College of Engineering Outstanding Graduate Student Award. From 2002 to 2004, he was a research engineer in the Panasonic Research and Development Center, China. He was a summer intern with the Thomson Corporate Research, Princeton, NJ, in 2006, working on digital video watermarking. His research interests include multimedia communication and security, image/video coding and watermarking, and distributed source coding.