DCANet: CNN model with dual-path network and improved coordinate attention for JPEG steganalysis

Abstract

Nowadays, convolutional neural network (CNN) is applied to JPEG steganalysis and performs better than traditional methods. However, almost all JPEG steganalysis methods utilize single-path structures, making it challenging to use the extracted noise residuals fully. On the other hand, most existing steganalysis detectors lack a focus on areas where secret information may be hidden. In this research, we present a steganalysis model with a dual-path network and improved coordinate attention to detect adaptive JPEG steganography, mainly including noise extraction, noise aggregation, and classification module. Especially, a dual-path network architecture simultaneously combining the advantages of both residual and dense connection is utilized to explore the hidden features in-depth while preserving the stego signal in the noise extraction module. Then, an improved coordinate attention mechanism is introduced into the noise aggregation module, which helps the network identify the complex texture area more quickly and extract more valuable features. We have verified the validity of some components through extensive ablation experiments with the necessary descriptions. Furthermore, we conducted comparative experiments on BOSSBase and BOWS2, and the experimental results demonstrate that the proposed model achieves the best detection performance compared with other start-of-the-art methods.