Towards multi-class blind steganalyzer for JPEG images

In this paper, we use the previously proposed calibrated DCT features [9] to construct a Support Vector Machine classifier for JPEG images capable of recognizing which steganographic algorithm was used for embedding. This work also constitutes a more detailed evaluation of the performance of DCT features as in [9] only a linear classifier was used. The DCT features transformed using Principal Component Analysis enable an interesting visualization of different stego programs in a three-dimensional space. This paper demonstrates that, at least under some simplifying assumptions in which the effects of double compression are ignored, it is possible to reliably classify stego images to their embedding techniques. The classifier is capable of generalizing to previously unseen techniques.