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Using Expanded Markov Process and Joint Distribution Features for JPEG Steganalysis

In this paper, we propose a scheme for detecting the information-hiding in multi-class JPEG images by combining expanded Markov process and joint distribution features. First, the features of the condition and joint distributions in the transform domains are extracted (including the Discrete Cosine Transform or DCT, the Discrete Wavelet Transform or DWT); next, the same features from the calibrated version of the testing images are extracted. A Support Vector Machine (SVM) is applied to the differences of the features extracted from the testing image and from the calibrated version. Experimental results show that this approach delivers good performance in identifying several hiding systems in JPEG images.