Deep Learning Image Steganalysis Method Fused with CBAM

Abstract

Steganography is a critical technical tool for preventing the disclosure of sensitive information. The detection performance of picture steganography algorithms based on deep learning has to be enhanced in tandem with the ongoing improvement of adaptive steganography algorithm performance. In this paper, a new model SRNet-CBAM based on SRNet fusion channel attention module and spatial attention module was presented to address the challenge of adaptive steganography analysis and the difficulty of model focused analysis for picture favorable regions. In three different embedding rates of WOW, S-UNIWARD, and HUGO algorithms, the experimental results reveal that the SRNet-CBAM model increases the accuracy of the SRNet model by 1.36% on average.