A Convolutional Neural Network Steganalysis Method Based on ShuffleBottleneck and Attention Mechanism

Hao Li, Xiangyang Luo, Yi Zhang, Qianqian Zhang, Chunfang Yang

Abstract: The steganography detection method based on deep learning fuse feature extraction and classification into one model, which reduces the human intervention in feature extraction and obtains a higher detection accuracy than the traditional steganography detection method based on manual feature extraction. However, many existing steganography detection methods based on deep learning generally need to increase the depth and width of the model to further improve the detection accuracy, but it also brings the increase of parameters and Flops (FLoating point OPerations) of the model, resulting in a large consumption of computing resources. Therefore, this manuscript proposes a Convolutional Neural Network(CNN) steganography detection method based on ShuffleBottleneck and Attention mechanism (referred to as ShuffleBANet method, ShuffleBottleneck-Attention-Network Based Method). First, to enhance the network's recognition ability for steganography signals, high-pass filters are used to enhance the steganography feature signals and combined with the improved ShuffleBottleneck structure to enrich the residual features and improve the expression ability of features. Then, a large convolution kernel is used to increase the convolution field of view, and the channel attention mechanism SE (Squeeze and Excitation) and the spatial attention mechanism CA(CoordAttention) are used to capture the residual features between channels and the location information of steganography signals, respectively. Combined with the siamese network framework, the feature extraction backbone is constructed. Finally, the features of the backbone are fused to increase the diversity of features, cover features and stego features are classified using the Softmax function. In this manuscript, BossBase-1.01, BOWS2 and ALASKA#2 datasets are used as cover images, the classical and latest steganalysis methods are used to conduct extensive experiments on the stego images generated by both spatial domain and JPEG domain adaptive steganography algorithms. The experimental results show that compared with the latest SiaStegNet method and the classical SRNet method, the detection efficiency of the proposed method is significantly improved, and the number of parameters and Flops has been reduced by 7.04% / 76.23% and 70.92% / 84.49%, respectively, which provides a solution for the lightweight steganography detection model based on deep learning.

External IDs:dblp:journals/tdsc/LiLZZY25