期刊
JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION
卷 88, 期 -, 页码 -出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jvcir.2022.103633
关键词
Steganalysis; Convolutional neural network; Channel attention; Convolutional pooling
资金
- National Key Research and Development Program of China [2020YFE0200600]
- Na-tional Natural Science Foundation of China [61571110]
This paper introduces a new approach for image steganalysis based on convolutional neural networks that focuses on complex regional texture features and improves detection accuracy. Experimental results demonstrate that the proposed model outperforms existing models in terms of detection accuracy.
Image steganalysis based on convolutional neural networks(CNN) has attracted great attention. However, existing networks lack attention to regional features with complex texture, which makes the ability of discrimination learning miss in network. In this paper, we described a new CNN designed to focus on useful features and improve detection accuracy for spatial-domain steganalysis. The proposed model consists of three modules: noise extraction module, noise analysis module and classification module. A channel attention mechanism is used in the noise extraction module and analysis module, which is realized by embedding the SE(Squeeze-and-Excitation) module into the residual block. Then, we use convolutional pooling instead of average pooling to aggregate features. The experimental results show that detection accuracy of the proposed model is significantly better than those of the existing models such as SRNet, Zhu-Net and GBRAS-Net. Compared with these models, our model has better generalization ability, which is critical for practical application.
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