JPEG steganalysis based on ResNeXt with Gauss partial derivative filters

Abstract

The latest research indicates that the image steganalysis has been greatly promoted by convolutional neural networks (CNNs). This study further addresses the problem of JPEG steganalysis through proposing a novel CNN architecture in which Gauss partial derivative (GPD) filters and two constructed blocks based on ResNeXt are integrated. In the proposed network, multi-order GPD filters are designed as the pre-processing layer to generate residual images, which can effectively capture sufficient embedding disturbance in texture and edge regions. Furthermore, referring to ResNeXt, two multi-branch blocks are constructed and aggregated to fully exploit the residual images to generate image features for classification. Numerous experiments have been conducted against J-UNIWARD on the public dataset to demonstrate the effectiveness and remarkable performance of the proposed network. Experimental results prove that the proposed network makes better performance than state-of-the-art CNN-based method J-Xu-Net and SCA-GFR. Source code is available via GitHub: \nhttps://github.com/Ante-Su/RXGNet\n\n.