Abstract
Spatial rich model (SRM) is a classic steganalysis method, which collects high-order co-occurrences from truncated noise residuals as feature to capture the local-range dependencies of an image. Increasing the truncation threshold and the co-occurrence order will lead to a higher-dimensional feature, which can exploit more statistical bins and capture dependencies across larger-range neighborhood, but this will suffer from the curse of dimensionality. In this paper, we propose a fast projection method to increase the statistical robustness of the higher-dimensional SRM feature while decreasing its dimensionality. The proposed projection method is applicable to co-occurrence-based steganalysis features. The detection performance and the computational complexity of the proposed method are investigated on three content-adaptive steganographic algorithms in spatial domain.
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Acknowledgments
The authors would like to thank the Network Center of Anhui University of Technology (AHUT) for providing cloud services to support this work.
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This study was funded by National Natural Science Foundation of China (Grant Nos. 61302178, 61105020), Foundation for Major Program of Education Bureau of Anhui Province (Grant No. KJ2015ZD09) and Excellent Youth Foundation of Anhui University of Technology (Grant No. z10097).
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All the authors and the Network Center of Anhui University of Technology declare that they have no conflict of interest.
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This article does not contain any studies with human participants or animals performed by any of the authors.
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Communicated by V. Loia.
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Wang, P., Wei, Z. & Xiao, L. Fast projections of spatial rich model feature for digital image steganalysis. Soft Comput 21, 3335–3343 (2017). https://doi.org/10.1007/s00500-015-2011-z
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DOI: https://doi.org/10.1007/s00500-015-2011-z