Multi-class blind steganalysis for JPEG images

In this paper, we construct blind steganalyzers for JPEG images capable of assigning stego images to known steganographic programs. Each JPEG image is characterized using 23 calibrated features calculated from the luminance component of the JPEG file. Most of these features are calculated directly from the quantized DCT coefficients as their first order and higher-order statistics. The features for cover images and stego images embedded with three different relative message lengths are then used for supervised training. We use a support vector machine (SVM) with Gaussian kernel to construct a set of binary classifiers. The binary classifiers are then joined into a multi-class SVM using the Max-Win algorithm. We report results for six popular JPEG steganographic schemes (F5, OutGuess, Model based steganography, Model based steganography with deblocking, JP Hide&Seek, and Steghide). Although the main bulk of results is for single compressed stego images, we also report some preliminary results for double-compressed images created using F5 and OutGuess. This paper demonstrates that it is possible to reliably classify stego images to their embedding techniques. Moreover, this approach shows promising results for tackling the difficult case of double compressed images.