A Steganalysis Method Based On Quantization Attack

We have addressed the problem of image steganalysis that have been potentially subjected to steganographic algorithms, both within the passive warden and active warden frameworks. In actual practice, most techniques produce stego images that are perceptually identical to the cover images but exhibit statistical irregularities that distinguish them from cover images. Statistical steganalysis exploits these irregularities in order to provide the best discrimination between cover and stego images. Attacking the cover-images and stego-images using the quantization method, we can obtained statistically different from embedded-and-quantization attacked images and from quantization attacked-but-not-embedded sources. We have developed a technique based on one-class SVM for discriminating between cover-images and stego-images. Simulation results with the chosen feature set and well known watermarking and steganographic techniques indicate that our approach is able with reasonable accuracy to distinguish between cover and stego images.