An analysis of quality factor on image steganalysis

Internet has emerged as a popular medium of communication, not only for common people but also for criminals. Various modes of covert communication are adopted by such elements. Steganography hides the information in digital images without changing their visual perception. These images can then be transferred over internet, without causing any suspicion to an observer. Steganalysis techniques inspect the images and detect the presence of hidden messages. In this paper, we have investigated the effect of quality factor of JPEG images on steganalysis using Discrete Cosine Transform. Features are extracted in Discrete Cosine Transform domain using higher order statistics, and subsequently a Fisher Linear Discriminant classifier is trained using different clean and steganographic images at different JPEG compression ratios i.e., quality factor. The quantitative results of experiments demonstrate that a classifier trained on particular quality factor gives better detection accuracy for images compressed with the same or a lower quality factor, but gives degraded performance of detection for images compressed with higher quality factor. This inference can aid in developing improved quality independent steganalysis mechanisms.