Prof. Chin-Chen Chang, Feng Chia University,Taiwan (IEEE Fellow)
Professor Chin-Chen Chang obtained his Ph.D. degree in computer engineering from National Chiao Tung University. His first degree is Bachelor of Science in Applied Mathematics and master degree is Master of Science in computer and decision sciences. Both were awarded in National Tsing Hua University. Dr. Chang served in National Chung Cheng University from 1989 to 2005. His current title is Chair Professor in Department of Information Engineering and Computer Science, Feng Chia University, from Feb. 2005. Prior to joining Feng Chia University, Professor Chang was an associate professor in Chiao Tung University, professor in National Chung Hsing University, chair professor in National Chung Cheng University. He had also been Visiting Researcher and Visiting Scientist to Tokyo University and Kyoto University, Japan. During his service in Chung Cheng, Professor Chang served as Chairman of the Institute of Computer Science and Information Engineering, Dean of College of Engineering, Provost and then Acting President of Chung Cheng University and Director of Advisory Office in Ministry of Education, Taiwan. Professor Chang's specialties include, but not limited to, data engineering, database systems, computer cryptography and information security. A researcher of acclaimed and distinguished services and contributions to his country and advancing human knowledge in the field of information science, Professor Chang has won many research awards and honorary positions by and in prestigious organizations both nationally and internationally. He is currently a Fellow of IEEE and a Fellow of IEE, UK. And since his early years of career development, he consecutively won Institute of Information & Computing Machinery Medal of Honor, Outstanding Youth Award of Taiwan, Outstanding Talent in Information Sciences of Taiwan, AceR Dragon Award of the Ten Most Outstanding Talents, Outstanding Scholar Award of Taiwan, Outstanding Engineering Professor Award of Taiwan, Chung-Shan Academic Publication Awards, Distinguished Research Awards of National Science Council of Taiwan, Outstanding Scholarly Contribution Award of the International Institute for Advanced Studies in Systems Research and Cybernetics, Top Fifteen Scholars in Systems and Software Engineering of the Journal of Systems and Software, Top Cited Paper Award of Pattern Recognition Letters, and so on. On numerous occasions, he was invited to serve as Visiting Professor, Chair Professor, Honorary Professor, Honorary Director, Honorary Chairman, Distinguished Alumnus, Distinguished Researcher, Research Fellow by universities and research institutes. He also published over serval hundred papers in Information Sciences. In the meantime, he participates actively in international academic organizations and performs advisory work to government agencies and academic organizations.
Speech Title：Embedding Secret Information in Digital Images Using Magic Turtle Shells
Abstract: Steganography is the science of secret message delivery using cover media. A digital image is a flexible medium used to carry a secret message because the slight modification of a cover image is hard to distinguish by human eyes. In this talk, I will introduce some novel steganographic methods based on different magic matrices. Among them, one method that uses a turtle shell magic matrix to guide cover pixels’ modification in order to imply secret data is the newest and the most interesting one. Experimental results demonstrated that this method, in comparison with previous related works, outperforms in both visual quality of the stego image and embedding capacity. In addition, I will introduce some future research issues that derived from the steganographic method based on the magic matrix. .
Assoc. Prof. Lap-Pui Chau, Nanyang Technological University, Singapore (IEEE Fellow)
Lap-Pui Chau received the Bachelor degree from Oxford Brookes University, and the Ph.D. degree from The Hong Kong Polytechnic University, in 1992 and 1997, respectively. He is an associate professor in School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. His research interests include fast visual signal processing algorithms, light-field imaging, video analytics for intelligent transportation system, and human motion analysis.
He was a General Chairs for IEEE International Conference on Digital Signal Processing (DSP 2015) and International Conference on Information, Communications and Signal Processing (ICICS 2015). He was a Program Chairs for International Conference on Multimedia and Expo (ICME 2016), Visual Communications and Image Processing (VCIP 2013) and International Symposium on Intelligent Signal Processing and Communications Systems (ISPACS 2010).
He was the chair of Technical Committee on Circuits & Systems for Communications (TC-CASC) of IEEE Circuits and Systems Society from 2010 to 2012. He served as an associate editor for IEEE Transactions on Multimedia, IEEE Signal Processing Letters, IEEE Transactions on Circuits and Systems for Video Technology, and is currently serving as an associate editor for IEEE Transactions on Circuits and Systems II, IEEE Transactions on Broadcasting, and The Visual Computer (Springer Journal). Besides, he was an IEEE Distinguished Lecturer for 2009-2016, and a steering committee member of IEEE Transactions for Mobile Computing from 2011-2013. He is an IEEE Fellow.
Speech Title: How to get Clear Vision in the Rain
Abstract: Rain removal is important for improving the robustness of outdoor vision based systems. Current rain removal methods show limitations either for complex dynamic scenes shot from fast moving cameras, or under torrential rain fall with opaque occlusions. In this talk, we cover a novel derain algorithm which applies superpixel (SP) segmentation to decompose the scene into depth consistent units. Alignment of scene contents are done at the SP level, which proves to be robust towards rain occlusion and fast camera motion.
Two alignment output tensors provide informative clues for rain streak location and occluded background contents to generate an intermediate derain output. These tensors can be subsequently prepared as input features for a convolutional neural network to restore high frequency details to the intermediate output for compensation of misalignment blur. Video demonstration will be showed for cleaner rain removal is achieved especially for highly dynamic scenes with heavy and opaque rainfall from a fast moving camera.
Prof. Xinpeng Zhang, Fudan University, China
Xinpeng Zhang received the B.S. degree in computational mathematics from Jilin University, China, in 1995, and the M.E. and Ph.D. degrees in communication and information system from Shanghai University, China, in 2001 and 2004, respectively. Since 2004, he had been with the faculty of the School of Communication and Information Engineering, Shanghai University, and he is currently a Professor of School of Computer Science, Fudan University. He was with the State University of New York at Binghamton as a visiting scholar from January 2010 to January 2011, and Konstanz University as an experienced researcher sponsored by the Alexander von Humboldt Foundation from March 2011 to May 2012. He served IEEE Transactions on Information Forensics and Security as an Associate Editor from 2014 to 2017. His research interests include multimedia security, image processing, and digital forensics. He has published more than 200 papers in these areas.
Speech Title: Signal Processing in Encrypted Domain
Abstract: Signal processing in encrypted domain is an emerging technology aimed at processing encrypted signals without revealing the plaintext content. In conventional combination of signal processing and encryption, the signal processing is always before encryption or after decryption, so that the processer is capable of accessing the signal to be processed. Signal processing in encrypted domain significantly differs from the conventional way and implies a new fashion of secure multimedia services, which is essentially suitable for cloud environment. What a content owner should do is only the encryption for privacy protection, and a server without the cryptographic key performs various processing algorithms on encrypted signals and provides the processed encrypted results. Then, a client having the cryptographic key may realize decryption to get the plaintext results. In the scenario, the storage and computation resources on the cloud can be fully exploited and the signal privacy is also protected. This tutorial will survey the concepts, principles and methods of signal processing in encrypted domain. Also, several important topics will be sufficiently discussed: signal transforms in encrypted domain, compression of encrypted signals, encrypted image retrieval and reversible data hiding in encrypted images.