Rate-Unknown Image Steganalysis Based on Dempster-Shafer Evidence Theory

The existing detection algorithms are difficult to obtain high detection accuracy when applied to the condition, in which the embedding rate of the stego-images is unknown. Therefore, it is very difficult to adopt the existing image steganalysis techniques in the design of a practical covert communication detection system. In this paper, combining fusion of decision-making technique", "we attempt to propose a rate-unknown image steganalysis scheme based on Dempster-Shafer(D-S) evidence theory. First, we can obtain various classifying results by establishing multi-rate training models, and then enhance these classifying results through introducing weighted coefficients according to their influences on the steganalysis. In the end, the final decision-making result is obtained by Dempster's combinational rule based on weighted coefficients. The experimental results on detecting LSB matching show that the proposed method can not only significantly improve the performance of detecting the rate-unknown stego images, but also reduce the false alarm rate. Moreover, it can control the classification effect by adjusting parameters.