Spatial rich model steganalysis feature normalization on random feature-subsets
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Spatial rich model (SRM) steganalysis feature is formed by high-order statistics collected from image noise residuals. These statistics are simply rescaled before machine learning. It is noted that SRM features of different cover images are very different. In this paper, we propose a feature normalization method based on random feature-subsets (NRS) for SRM. We randomly draw feature-subsets from SRM feature. Then these feature-subsets are normalized by using per-sample rescaling method to make the feature-subsets of different images have the same 1-norm (the sum of all elements). The proposed NRS method can adjust the feature distribution and increase the feature diversity. The normalized feature-subsets can achieve better detection performance and also can be used as a useful complement for the existing steganalysis features. Experimental results show that: (1) a small amount of normalized feature-subset supplement can obviously improve the detection performance of SRM feature; (2) under the same dimensionality, the proposed NRS version feature can achieve a better detection accuracy than that of original feature; (3) the proposed NRS method is applicable to projections of spatial rich model feature; (4) compared with steganalysis feature extraction, the computational time of NRS can be negligible.
KeywordsSteganalysis Co-occurrence Feature normalization Random feature-subsets
The authors would like to thank the Network Center of Anhui University of Technology (AHUT) for providing cloud services to support this work. 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).
Compliance with ethical standards
Conflict of interest
All the authors and the Network Center of Anhui University of Technology declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
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