Abstract

In recent years, digital data hiding has emerged as an increasingly active research area. Information can be hidden into images, videos, and audios imperceptibly to human beings, thus providing vast opportunities for covert communications. Consequently, methods to detect covert communications are called for. This task is especially urgent for law enforcement to deter the illegal distribution of children pornographic images/videos hidden inside normal images/videos, and for intelligence agencies to intercept criminal communications among terrorists. In addition to detect whether a given medium has hidden message in it, steganalysis can also serve as an effective way to judge the security performance of steganographic techniques. In this talk, the basic concepts and the current status of steganography and steganalysis are first introduced. A newly developed general blind steganalysis system is then presented with some detail. It can detect if some message has been hidden inside a given image without any prior knowledge of the data embedding technique that has possibly been used and without having the original image in the detection. The statistical moments of characteristic functions of the prediction-error image, the test image, and their wavelet subbands are selected as features. Support vector machines (SVMs) and artificial neural network (ANN) have been utilized as the classifier in our investigation. The performance of the proposed steganalysis system in our initial yet extensive experimental works is shown significantly superior to the prior arts in terms of detection rate. The proposed steganalysis system with five typical data hiding schemes being jointly considered and trained has pointed out a promising direction for effective blind steganalysis. Finally several difficult and challenging future research issues are discussed.

Biography

Dr. Yun Q. Shi is a professor of Electrical and Computer Engineering at New Jersey Institute of technology. He obtained his B.S. degree and M.S. degree from the Shanghai Jiao Tong University, Shanghai, China; his M.S. and Ph.D. degrees from the University of Pittsburgh, PA. His research interests include visual signal processing and communications, digital multimedia data hiding and information assurance, applications of digital image processing, computer vision and pattern recognition to industrial automation and biomedical engineering, theory of multidimensional systems and signal processing. Some of his research projects are currently supported by several federal and New Jersey State funding agencies. He is an author/coauthor of more than 170 papers in his research areas, a book on Image and Video Compression, three book chapters on Image Data Hiding and one book chapter on Digital Image Processing. He holds two US patents and has eight US patents pending. Dr. Shi is the chairman of Signal Processing Chapter of IEEE North Jersey Section, an editorial board member of International Journal of Image and Graphics, the founding editor-in-chief of Springer LNCS Transactions of Data Hiding and Multimedia Security, a member of IEEE CASS Technical Committee of Visual Signal Processing and Communications, Technical Committee of Multimedia Systems and Applications, and Technical Committee of Life Science, Systems and Applications, a member of IEEE SPS Technical Committee of Multimedia Signal Processing, an Associate Editor of IEEE Transactions on Circuits and Systems II. He was an IEEE CASS Distinguished Lecturer, a co-general chair of IEEE 2002 International Workshop on Multimedia Signal Processing, a formal reviewer of the Mathematical Reviews, an Associate Editor of IEEE Transactions on Signal Processing, the guest editor of several special issues on several journals, one of the contributing authors in the area of Signal and Image Processing to the Comprehensive Dictionary of Electrical Engineering. He is a Fellow of IEEE.

For Further Information: Contact Yun Shi (973)-596-3501, shi@njit.edu, Alfredo Tan (201) 692- 2347, tan@fdu.edu, or Hong Man (201)-216-5038, hman@stevens-tech.edu.