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A study on Subtractive Pixel Adjacency Matrix features

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Abstract

Subtractive Pixel Adjacency Matrix (SPAM) features perform well in detecting spatial-domain steganographic algorithm. Further, some methods of SPAM features can be applied to rich models and steganalysis based on deep learning. Therefore, this paper presents a study on SPAM features and it is divided into two parts: in the first part, impact of spatial-domain steganographic on difference between adjacent pixels is first analyzed. Then, three SPAM features are proposed with the same range of differences and different orders of Markov chain. Following that, the influences of order of Markov chain and range of differences on SPAM features are analyzed, and we find that detection accuracy of SPAM features increases with the range of differences increasing; in the second part, SPAM feature is first divided into several modules according to the conclusion. Then, taking detection accuracy of support vector machine (SVM) classifier and mutual information as metrics and module as a unit, a Novel Feature Selection (NFS) algorithm and an Improved Feature Selection algorithm are proposed. Experimental results show that the NFS algorithm can achieve higher detection accuracy than several existing algorithms.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (61771334).

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  1. School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China

    Xiangyuan Gu & Jichang Guo

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  1. Xiangyuan Gu
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  2. Jichang Guo
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Correspondence to Jichang Guo.

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Gu, X., Guo, J. A study on Subtractive Pixel Adjacency Matrix features. Multimed Tools Appl 78, 19681–19695 (2019). https://doi.org/10.1007/s11042-019-7285-1

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  • DOI: https://doi.org/10.1007/s11042-019-7285-1

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