Statistical Restoration to Resist Chi-Square Attack Based on Heuristic Algorithms in Steganalytic Systems

Steganalysis are used to detect whether an image contains a hidden message. By analyzing different image features between stego images and cover-images, a steganalyzer is able to detect stego images. In this paper, we present a new method in LSB embedding by avoiding the change of statistic features. After embedding data in the first LSB bits, we apply Particle Swarm Optimization by adjusting the second LSB bits of a stego image while creating the desired statistic features to generate the modified stego images that can break the inspection of chi-square attack. Experimental results show that our algorithm can not only pass the detection of chi-square test, but also leave our hidden message unchanged on the first LSB bit, and enhance the peak signal-to-noise ratio of stego images. By using PSO algorithm, speed of convergence also is improved.