Theory And Method Of Steganographic Countermeasure

For thousands of years, it has always been a significant research problem to seek secure ways for communicating sensitive information. With the rapid development of networks and multimedia technology, the ancient information hiding method has been rediscovered and received considerable attention, for concealing the very presence of secret communication in digital world is much easier. Consequently, the "hide and seek" problem under this secure communication paradigm, which is also called the "countermeasure between steganography and steganalysis", is becoming a hot topic for academic research in recent years. Aiming at the various problems in this field, a systematic study on steganographic countermeature has been made on the aspects of "security theory", "steganographic method" and "steganalytic method" respectively. The main contribution of this dissertation is as follows:As to the study of security theory, an analysis on the limitation of current theories and models on steganographic security is presented firstly, and then the difference between "stego data security" and "steganographic system (algorithm) securiy" is clearified. Under the perspective of comprehensive information theory, the connotation of steganographic security is rediscussed and a conclusion is drawn that steganography originates from the limited cognitive ability of subject to object in the sense of comprehensive information. Furthermore, a novel framework of steganographic security is constructed based on the concept of "cover distribution boundary", and some essential concepts are explicitly defined, including stego data security, steganographic system securiy and secure steganographic capacity of cover data. Finally, according to whether the steganographic algorithm is released or not, two security measurement models under this framework are presented, respectively.As to the steganographic model and method, the design principles of current typical steganographic technologies are analyzed at first. Moreover, heuristic steganography and constrained steganography are differentiated on the basis of different design ideas, with the former constrained by security measurement models and the latter originated from experiential understanding of security. In addition, a steganographic model constrained by statistical security is put forward and some key questions about heuristic steganography are discussed. Finally, a novel heuristic steganographic method and a novel constrained steganographic method are proposed, utilizing histogram preprocessing strategy and binary particle swarm optimization strategy respectively. As to the steganalytic model and method, different design ideas are summarized from the methodological point of view, and steganalytic technologies are reclassified as probabilistic deduction steganalysis and statistical induction steganalysis (statistical steganalysis for short). Furthermore, mathematical description of qualitative steganalysis and quantitative steganalysis are presented respectively under the category of statistical steganalysis. On the basis of these work, a series of novel steganalytic methods are proposed applying statistical learning theory in finite sample space, including steganalytic discriminant method based on cover distribution boundary, source forensic method based on multi-classification, quantitative steganalytic method based on multivariate regression and quantitative steganalytic method based on support vector regression. Finally, three approaches are studied to further improve steganalytic performance from the angles of holistic statistical features, ensemble learning and the scale of training database.