Image steganalysis feature selection based on the improved Fisher criterion

Yuanyuan Ma; Jinwei Wang; Xiangyang Luo; Zhenyu Li; Chunfang Yang; Jun Chen; Yuanyuan Ma; Jinwei Wang; Xiangyang Luo; Zhenyu Li; Chunfang Yang; Jun Chen

doi:10.3934/mbe.2020068

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 2: 1355-1371. doi: 10.3934/mbe.2020068

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Research article Special Issues

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

Received: 14 May 2019 Accepted: 09 October 2019 Published: 21 November 2019

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.
- steganalysis,
- feature selection,
- I-Fisher criterion,
- separability
Citation: Yuanyuan Ma, Jinwei Wang, Xiangyang Luo, Zhenyu Li, Chunfang Yang, Jun Chen. Image steganalysis feature selection based on the improved Fisher criterion[J]. Mathematical Biosciences and Engineering, 2020, 17(2): 1355-1371. doi: 10.3934/mbe.2020068

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Abstract

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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