Lightweight Steganography Detection Method Based on Multiple Residual Structures and Transformer

Existing deep learning-based steganography detection methods utilize convolution to automatically capture and learn steganographic features, yielding higher detection efficiency compared to manually designed steganography detection methods. Detection methods based on convolutional neural network frameworks can extract global features by increasing the network’s depth and width. These frameworks are not highly sensitive to global features and can lead to significant resource consumption. This manuscript proposes a lightweight steganography detection method based on multiple residual structures and Transformer (ResFormer). A multi-residuals block based on channel rearrangement is designed in the preprocessing layer. Multiple residuals are used to enrich the residual features and channel shuffle is used to enhance the feature representation capability. A lightweight convolutional and Transformer feature extraction backbone is constructed, which reduces the computational and parameter complexity of the network by employing depth-wise separable convolutions. This backbone integrates local and global image features through the fusion of convolutional layers and Transformer, enhancing the network’s ability to learn global features and effectively enriching feature diversity. An effective weighted loss function is introduced for learning both local and global features, BiasLoss loss function is used to give full play to the role of feature diversity in classification, and cross-entropy loss function and contrast loss function are organically combined to enhance the expression ability of features. Based on BossBase-1.01, BOWS2 and ALASKA#2, extensive experiments are conducted on the stego images generated by spatial and JPEG domain adaptive steganographic algorithms, employing both classical and state-of-the-art steganalysis techniques. The experimental results demonstrate that compared to the SRM, SRNet, SiaStegNet, CSANet, LWENet, and SiaIRNet methods, the proposed ResFormer method achieves the highest reduction in the parameter, up to 91.82%. It achieves the highest improvement in detection accuracy, up to 5.10%. Compared to the SRNet and EWNet methods, the proposed ResFormer method achieves an improvement in detection accuracy for the J-UNIWARD algorithm by 5.78% and 6.24%, respectively.