This paper presents an effective methodology for motion vector-based video steganography. The main principle is to design a suitable distortion function expressing the embedding impact on motion vectors by exploiting the spatial-temporal correlation based on the framework of minimal-distortion steganography. Two factors are considered in the proposed distortion function, which are the statistical distribution change (SDC) of motion vectors in spatial-temporal domain and the prediction error change (PEC) caused by modifying the motion vectors. The practical embedding algorithm is implemented using syndrome-trellis codes (STCs). Experimental results show that the proposed method can enhance the security performance significantly compared with other existing motion vectorbased video steganographic approaches, while obtaining the higher video coding quality as well.
In current media-centric society, video has become the suitable cover for steganography. However, video steganalysis remains largely unexplored compared to the mature image steganalysis. One open problem is how to design efficient rich features for video steganalysis by exploiting the correlations in video. In this paper, we propose a novel video steganalytic scheme based on the spatial-temporal correlation of motion vectors. The proposed scheme employs 324-dimension features from Markov matrix of motion vectors in each sliding window constituting of eight inter-coded frames without overlapping. Experimental results show that our proposed scheme performs better in detecting existing motion vector-based steganographic methods than previous related steganalysis schemes.
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