Abstract
The SRM (Spatial Rich Model) is a very effective steganalysis method. It uses statistics of neighboring noise residual samples as features to capture the dependency changes caused by embedding. Because the noise residuals are the high-frequency components of image and closely tied to image content, the residuals of different types of image regions have different statistical properties and effectiveness for steganalysis. In this paper, the effectiveness of the residuals is investigated. Then the effectiveness of the statistics collected from different types of neighboring residual samples is investigated from the FLD (Fisher Linear Discriminant) viewpoint, and ineffective, effective and high-effective neighboring residual samples are defined. The ineffective neighboring residual samples are not likely to change during embedding, and if they are counted in statistics, they may mix the features with noise and make the features impure. Pure SRM features are extracted based on neighboring noise residual sample selection strategy. Furthermore, multi-order statistical features are proposed to increase the statistical diversity. Steganalysis performances of the statistical features collected from different types of neighboring residual samples are investigated on three content adaptive steganographic algorithms. Experimental results demonstrate that the proposed method can achieve a more accurate detection than SRM.
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Acknowledgments
This work was supported by the Natural Science Foundation of China (No.61302178 and 61402009), and the Excellent Youth Foundation of Anhui University of Technology (AHUT) (No. QZ201014). 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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Wang, P., Wei, Z. & Xiao, L. Pure spatial rich model features for digital image steganalysis. Multimed Tools Appl 75, 2897–2912 (2016). https://doi.org/10.1007/s11042-015-2521-9
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DOI: https://doi.org/10.1007/s11042-015-2521-9