Double Compression Detection Based On Feature Fusion

This paper proposes a feature fusion based method for JPEG double compression detection. When extracting the fusion features, the Markov model of the first digits of DCT coefficients and the difference of adjacent coefficients extracted from JPEG images are fused to a big feature vector, and then the Sammon mapping method is applied for dimensionality reduction. Finally, the Support Vector Machine is applied to detect the JPEG double compression based on the feature fusion. The experimental result indicates that for the images compressed by QF1=QF2 or QF1=95, our method can still achieve a relative high classification accuracy which most of the previous algorithms have reported to be powerless.