Comprehensive Criteria-Based Generalized Steganalysis Feature Selection Method

Abstract

Redundant steganalysis feature components in high-dimensional steganalysis feature of images increase the spatio-temporal complexity of steganalysis and even reduce the detection accuracy of the stego images. In order to reduce the image steganalysis feature dimension, improve the detection accuracy of the stego images and achieve fast feature selection, this paper proposes a general method for image steganalysis feature selection. Firstly, a feature metric algorithm based on the difference function is given, and this algorithm measures the difference of the steganalysis feature components between the cover image class and the stego image class, which provides the basis for selecting the steganalysis feature components contributing greatly to detect the stego images. Secondly, the Pearson correlation coefficient is improved and used to measure the correlation between the steganalysis feature components and the image classification results to provide the basis for removing the redundant steganalysis feature components. And then, by setting thresholds, the steganalysis feature components with a large difference function value are selected and with a small Pearson correlation coefficient are deleted. Finally, the steganalysis feature components retained are trained and detected as the final steganalysis feature. A series of experimental results indicate that this method can reduce the feature dimension effectively and the spatio-temporal complexity of steganalysis, while maintaining or even improving the detection accuracy of the stego images. Compared to existing steganalysis feature selection methods such as Fisher-GFR and Improved-Fisher, this method has a higher detection accuracy of the stego images after simplification.

Keywords

• Comprehensive criteria
• difference function
• feature selection
• Pearson correlation coefficient
• steganalysis feature component