Feature Selection of the Rich Model Based on the Correlation of Feature Components

Yuan Liu,Yuanyuan Ma,Chunfang Yang,Yuewei Dai,Shunhao Jin,Fenlin Liu

doi:10.1155/2021/6680528

Abstract

Currently, the popular Rich Model steganalysis features usually contain a large number of redundant feature components which may bring “curse of dimensionality” and large computation cost, but the existing feature selection methods are difficult to effectively reduce the dimensionality when there are many strongly correlated effective feature components. This paper proposes a novel selection method for Rich Model steganalysis features. First, the separability of each feature component in the submodels of Rich Model is measured based on the Fisher criterion, and the feature components are sorted in the descending order based on the separability. Second, the correlation coefficient between any two feature components in each submodel is calculated, and feature selection is performed according to the Fisher value of each component and the correlation coefficients. Finally, the selected submodels are combined as the final steganalysis feature. The results show that the proposed feature selection method can effectively reduce the dimensionalities of JPEG domain and spatial domain Rich Model steganalysis features without affecting the detection accuracies.

Highlights

Digital steganography is a technology that embeds information in the redundancy of digital images, audio, video, text, and so on to achieve the purpose of covert communication [1,2,3,4,5,6]
The above methods can significantly reduce the dimensionalities of Rich Model steganalysis features
The proposed feature selection method was tested on typical JPEG image steganalysis feature CC-JRM and spatial image steganalysis feature SRM for the steganography algorithms J-UNIWARD and S-UNIWARD

Summary

Introduction

Digital steganography is a technology that embeds information in the redundancy of digital images, audio, video, text, and so on to achieve the purpose of covert communication [1,2,3,4,5,6]. With the presentation of the HUGO steganography algorithm [7] in 2010, adaptive steganography based on the framework of “distortion function + STC coding” has become the mainstream of image steganography. Based on this framework, researchers have successively proposed a series of adaptive steganography algorithms with high antidetection performance, which make the traditional steganalysis algorithms mostly invalid [8,9,10,11,12]. In 2012, Fridrich and Kodovskyproposed the Rich Model steganalysis feature [13], which effectively improved the detection performance for HUGO steganography. Many features with thousands of dimensions have been successively proposed for steganalysis, such as PSRM (Project Spatial Rich Model) features [14], PHARM (PhaseAware Projection Rich Model) features [15], GFR (Gabor Filter Rich Model) features [16], and CC-JRM (CartesianCalibrated JPEG Rich Model) features [17]. ese features may bring large computation and storage costs and even the problem of “curse of dimensionality.” In order to reduce the dimensionality of the steganalysis feature, researchers have carried out a series of works in two different ways containing feature transformation and feature selection

Methods

Results

Conclusion