JPEG image steganalysis method based on binary similarity measures

Many JPEG steganography techniques are used to communicate secret messages by terrorists that threaten the security of a nation. So steganalysis is very important. Avcibas proposed a steganalysis based on binary similarity measures which only work well on LSB-based steganography and derived features from the spatial domain of images. This paper proposes a novel usage of binary similarity measures in JPEG steganalysis. The method captures the seventh and eighth bit planes of the non-zero DCT coefficients from JPEG images and computes 14 features of each image based on binary similarity measures. These features are used to construct a support vector machine classifier which can distinguish between stego images and cover images. The experiment results are presented to demonstrate that the proposed scheme has lower computational complexity and the same high detecting accuracy.