Double JPEG Compression Detection Based on Markov Model.

In this paper, a feature based on the Markov model in quaternion discrete cosine transform (QDCT) domain is proposed for double JPEG compression detection. Firstly, a given JPEG image is extracted from blocked images to obtain amplitude and three angles (\(\psi \), \(\phi \), and \(\theta \)). Secondly, when extracting the Markov features, we process the transition probability matrix with the corresponding refinement. Our proposed refinement method not only reduces redundant features, but also makes the acquired features more efficient for detection. Finally, a support vector machine (SVM) is employed for NA-DJPEG compression detection. It is well known that detecting NA-DJPEG compressed images with QF1 \(\ge \) QF2 is a challenging task, and when the images with small size (i.e., 64 \(\times \) 64), the detection will be more difficult. The experimental result indicates that our method can still achieve a high classification accuracy in this case.