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Image steganalysis feature selection based on the improved Fisher criterion

1 State Key Laboratory of Mathematical Engineering and Advanced Computing, No. 62 Science Road, Zhengzhou 450001, China
2 Henan Normal University, No. 46 Jianshe Road, Xinxiang 453002, China
3 Nanjing University of Information Science and Technology, Nanjing 210044, China

Special Issues: Security and Privacy Protection for Multimedia Information Processing and communication

In order to improve the detection accuracy of hidden message in images, steganalysis features are selected as inputs for steganalysers. However, the existing Fisher criterion ignores the contribution of steganalysis feature components in dispersion to classification, which causes the useful feature components to be deleted, and decreases the detection accuracy of steganalysis features. By analyzing the separability of steganalysis feature components, we introduce the sigmoid function into Fisher’s criterion and propose an improved Fisher criterion (I-Fisher criterion), which can make up for the traditional Fisher criterion in separability measurement of steganalysis feature components. To optimize the steganalysis feature and reduce its dimension, we employ the improved Fisher criterion as the heuristic function of the decision rough set α-positive region reduction, and propose the feature selection method based on the improved Fisher. Experimental results show that the proposed method can reduce the dimension and memory of the GFR high-dimensional feature and the CC-PEV lowdimensional feature while maintaining or improving the detection accuracy.
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© 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

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