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Digital video steganalysis by subtractive prediction error adjacency matrix

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Abstract

Video has become an important cover for steganography for its large volume. There are two main categories among existing methods for detecting steganography which embeds in the spatial domain of videos. One category focuses on the spatial redundancy and the other one mainly focuses on the temporal redundancy. This paper presents a novel method which considers both the spatial and the temporal redundancy for video steganalysis. Firstly, model of spread spectrum steganography is provided. PEF (Prediction Error Frame) is then chosen to suppress the temporal redundancy of the video content. Differential filtering between adjacent samples in PEFs is employed to further suppress the spatial redundancy. Finally, Dependencies between adjacent samples in a PEF are modeled by a first-order Markov chain, and subsets of the empirical matrices are then employed as features for a steganalyzer with classifier of SVM (Support Vector Machine). Experimental results demonstrate that for uncompressed videos, the novel features perform better than previous video steganalytic works, and similar to the well-known SPAM (Subtractive Pixel Adjacency Model) features which are originally designed for image steganalysis. For videos compressed with distortion, the novel features perform better than other features tested.

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Acknowledgments

This research was supported by the National Natural Science Foundation of China (NSFC) under the grant No. 61170226, the Fundamental Research Funds for the Central Universities under the grant Nos.SWJTU11CX047, and Chengdu Science and Technology program under the grant No. 12DXYB214JH-002.

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Authors and Affiliations

  1. National Digital Switching System Engineering & Technological Research Center, Zhengzhou, China

    Keren Wang

  2. Science and Technology on Blind Signal Processing Laboratory, Chendu, China

    Keren Wang & Jiesi Han

  3. Southwest Jiaotong University, Chengdu, China

    Hongxia Wang

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  1. Keren Wang
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  2. Jiesi Han
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  3. Hongxia Wang
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Correspondence to Keren Wang.

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Wang, K., Han, J. & Wang, H. Digital video steganalysis by subtractive prediction error adjacency matrix. Multimed Tools Appl 72, 313–330 (2014). https://doi.org/10.1007/s11042-013-1373-4

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  • DOI: https://doi.org/10.1007/s11042-013-1373-4

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