Multi-level attention network for image steganalysis
Pokun Yang, Ke Qi, Shunyu Yao · 2022 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) · 2022
While attention mechanisms have achieved excellent performance in image recognition, target tracking and natural language processing, little studies have been done on attention based steganalysis. In order to verify the performance of attention mechanism in deep learning-based steganalysis, this paper presents the first image steganalysis model based on multilayer attention mechanism, which applies a dual-residual structure network to effectively enhance the transmission of steganographic signals in the deep network, and introduces a multilayer attention module to aggregate convolutional features and obtain channel attention through an interactive local cross-channel strategy, and to feed the feature map with the aggregated channel information by pooling into the convolutional layer to obtain spatial attention, effectively improving the model’s ability to capture steganographic data. The experimental results on the BOSSbasev1.01 dataset and BOWS2 dataset demonstrate the effectiveness of the proposed method, which achieves comparable or leading results compared with the current mainstream algorithms.