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.