Abstract
Steganography is a critical technical tool for preventing the disclosure of sensitive information. The detection performance of picture steganography algorithms based on deep learning has to be enhanced in tandem with the ongoing improvement of adaptive steganography algorithm performance. In this paper, a new model SRNet-CBAM based on SRNet fusion channel attention module and spatial attention module was presented to address the challenge of adaptive steganography analysis and the difficulty of model focused analysis for picture favorable regions. In three different embedding rates of WOW, S-UNIWARD, and HUGO algorithms, the experimental results reveal that the SRNet-CBAM model increases the accuracy of the SRNet model by 1.36% on average.
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Acknowledgement
This work is supported by the Hunan Provincial Natural Science Foundation of China (2021JJ50074), the Scientific Research Fund of Hunan Provincial Education Department (19B082), the Science and Technology Plan Project of Hunan Province (2016TP1020), the Subject Group Construction Project of Hengyang Normal University (18XKQ02), the Application oriented Special Disciplines, Double First- Class University Project of Hunan Province (Xiangjiaotong [2018] 469), the Hunan Province Special Funds of Central Government for Guiding Local Science and Technology Development (2018CT5001), the First Class Undergraduate Major in Hunan Province Internet of Things Major (Xiangjiaotong [2020] 248, No. 288).
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Chen, H., Jiao, G. (2022). Deep Learning Image Steganalysis Method Fused with CBAM. In: Liu, Q., Liu, X., Cheng, J., Shen, T., Tian, Y. (eds) Proceedings of the 12th International Conference on Computer Engineering and Networks. CENet 2022. Lecture Notes in Electrical Engineering, vol 961. Springer, Singapore. https://doi.org/10.1007/978-981-19-6901-0_123
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