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This paper presents a novel motion vector (MV) steganalysis method. MV-based steganographic methods exploite the variability of MV to embed messages by modifying MV slightly. However, we have noticed that the modified MVs after steganography cannot follow the optimal matching rule which is the target of motion estimation. It means that steganographic methods conflict with the basic principle of video compression. Aiming at this difference, we proposed a steganalysis feature based on Subtractive Probability of Optimal Matching(SPOM), which statistics the MV's Probability of the Optimal matching (POM) around its neighbors, and extract the classification feature by subtracting the POM of the test video and its recompressed video. Experiment results show that the proposed feature is sensitive to MV-based steganography methods, and outperforms the other methods, especially for high temporal activity video.

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