General expansion-shifting model for reversible data hiding: Theoretical investigation and practical algorithm
报告内容: Reversible data hiding (RDH) is a specific data hiding technique in which both the embedded secret data and the original cover image can be exactly extracted from the marked image. In this talk, we present a general expansion-shifting model for RDH by introducing the so-called reversible embedding function (REF). With REF, RDH can be designed and the corresponding rate-distortion formulations can be established, providing a possibility to optimize the reversible embedding performance. Optimal REF for one-dimensional histogram is introduced, and all optimal REF can be derived in this case when the maximum modification to the cover pixel is limited as a small value. Based on the derived optimal REF for one-dimensional histogram, a practical RDH scheme is presented as well.
报告人简介: Dr. Xiaolong Li received the B.S. degree from Peking University, the M.S. degree from Ecole Polytechnique (France), and the Ph.D. degree in mathematics from ENS de Cachan (France), in 1999, 2002, and 2006, respectively. He worked as a postdoctoral fellow and then a researcher at Peking University in 2007-2016. He is currently a Professor with the Institute of Information Science, Beijing Jiaotong University. His research interests are image processing and information hiding.