1月5日讲座《STATISTICAL MODEL BASED FRAGILE WATERMARKING AND ROBUST DATA HIDING IN MULTIMEDIA COMMUNICATIONS》
讲座题目：STATISTICAL MODEL BASED FRAGILE WATERMARKING AND ROBUST DATA HIDING IN MULTIMEDIA COMMUNICATIONS
讲座时间：2012年1月5日（周四）上午9：00
讲座地点：海韵园行政楼C座505报告厅
讲座摘要：
In this talk, I first give an overview for our work in Communication and Signal Processing Applications Laboratory (CASPAL) at Ryerson University. I will then discuss our work on multimedia communications, especially on watermarking and data hiding. Our methods are based on the statistical modeling of host data. First, a new multiscale fragile watermarking scheme based on the Gaussian mixture model (GMM) in the wavelet domain is presented. The GMM model parameters of different watermarking blocks are adjusted to form certain relationships as fragile watermarks for authentication. An optimal watermark embedding method is developed to achieve minimum watermarking distortion. A secret embedding key is designed to securely embed the fragile watermarks so that the new method is robust to counterfeiting, even when the malicious attackers are fully aware of the watermark embedding algorithm. This new statistical model based method modifies only a small amount of image data such that the distortion on the host image is imperceptible. The new method can detect and localize image tampering and help distinguish some normal image operations such as JPEG compression from malicious image attacks, and thus can be used for semi-fragile watermarking.
In the second part, we consider data hiding as a covet communication problem and present novel data hiding concepts using the minimum distortion look-up table (LUT) embedding that achieves good distortion-robustness performance by statistical analysis of the host data. We find that it is possible to optimally reduce the data hiding introduced distortion by designing the LUT according to the distribution of the host at a given robustness level. New practical data hiding schemes using the optimal LUT are developed to reduce the host data distortion. Theoretical analysis and numerical results show that the new LUT design achieves not only less distortion but also more robustness than the traditional LUT based data embedding schemes under various attacks such as Gaussian noise and JPEG compression.
讲座教授简介:
Xiao-Ping Zhang received the B.S. and Ph.D. degrees from Tsinghua University, in 1992 and 1996, respectively, all in electronic engineering. He holds an MBA in Finance, Economics and Entrepreneurship with Honors from the University of Chicago Booth School of Business.
Since Fall 2000, he has been with the Department of Electrical and Computer Engineering, Ryerson University, where he is now Professor, Director of Communication and Signal Processing Applications Laboratory (CASPAL) and Program Director of Graduate Studies. Prior to joining Ryerson, from 1996 to 1998, he was a postdoctoral fellow at the University of Texas, San Antonio and then at the Beckman Institute, the University of Illinois at Urbana-Champaign. He held research and teaching positions at the Communication Research Laboratory, McMaster University, in 1999. From 1999 to 2000, he was a Senior DSP Engineer at SAM Technology, Inc., at San Francisco, and a consultant at San Francisco Brain Research Institute. His research interests include multimedia communications and signal processing, sensor networks and electronic systems, multimedia content analysis, computational intelligence, and applications in bioinformatics, finance, and marketing. He is a frequent consultant for biotech companies and investment firms. He is cofounder and CEO for EidoSearch, Inc., which offers a content-based search and analysis engine for financial data.
Dr. Zhang is a registered Professional Engineer in Ontario, Canada, a Senior Member of IEEE and a member of Beta Gamma Sigma Honor Society. He is the publicity co-chair for ICME'06 and program co-chair for ICIC'05. He is currently an Associate Editor for IEEE Signal Processing Letters and for Journal of Multimedia.
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