KEYNOTE SPEAKERS

 

Prof. PUN Chi Man

Department of Computer and Information Science Faculty of Science and Technology, University of Macau Avenida da Universidade, Taipa, Macau, China

  

Prof. Pun received his Ph.D. degree in Computer Science and Engineering from the Chinese University of Hong Kong in 2002, and his M.Sc. and B.Sc. degrees from the University of Macau. He had served as the Head of the Department of Computer and Information Science, University of Macau from 2014 to 2019, where he is currently a Professor and in charge of the Image Processing and Pattern Recognition Laboratory. He has investigated many externally funded research Projects as PI, and has authored/co-authored more than 200 refereed papers in many top-tier Journals (including T-PAMI, T-IFS, T-IP, T-DSC, T-KDE, and T-MM) and Conferences (including CVPR, ICCV, ECCV, AAAI, ICDE, IJCAI, MM, and VR). He has also co-invented several China/US Patents, and is the recipient of the Macao Science and Technology Award 2014 and the Best Paper Award in the 6th Chinese Conference on Pattern Recognition and Computer Vision (PRCV2023). Dr. Pun has served as the General Chair for the 10th &11th International Conference Computer Graphics, Imaging and Visualization (CGIV2013, CGIV2014), the 13th IEEE International Conference on e-Business Engineering (ICEBE2016), and the General Co-Chair for the IEEE International Conference on Visual Communications and Image Processing (VCIP2020) and the International Workshop on Advanced Image Technology (IWAIT2022), and the Program/Local Chair for several other international conferences. He has also served as the SPC/PC member for many top CS conferences such as AAAI, CVPR, ICCV, ECCV, MM, etc. He has been listed in the World's Top 2% Scientists by Stanford University since 2020. His research interests include Image Processing and Pattern Recognition; Multimedia Information Security, Forensic and Privacy; Adversarial Machine Learning and AI Security, etc. He is also a senior member of the IEEE.

Speech Title: Image Splicing Localization with Deep Neural Networks

Abstract: Creating fake pictures has become more accessible than ever, but tampered images are more harmful because the Internet propagates misleading information so rapidly. Reliable digital forensic tools are, therefore, strongly needed. Traditional methods based on hand-crafted features are only useful when tampered images meet specific requirements, and the low detection accuracy prevents them from being used in realistic scenes. Recently proposed learning-based methods improve the accuracy, but neural networks usually require to be trained on large labeled databases. This is because commonly used deep and narrow neural networks extract high-level visual features and neglect low-level features where there are abundant forensic cues. In this talk, we will discuss some solutions to this problem. Two novel image splicing localization methods are proposed using deep neural networks, which mainly concentrate on learning low-level forensic features and consequently can detect splicing forgery, although the network is trained on a small automatically generated splicing dataset.