Image steganalysis based on model compression

Siyuan Huang; Minqing Zhang; Xiong Zhang; Chao Jiang; Yongjun Kong; Fuqiang Di; Yan Ke

doi:10.1117/12.3031915

6 June 2024 Image steganalysis based on model compression

Siyuan Huang, Minqing Zhang, Xiong Zhang, Chao Jiang, Yongjun Kong, Fuqiang Di, Yan Ke

Proceedings Volume 13175, International Conference on Computer Network Security and Software Engineering (CNSSE 2024); 1317517 (2024) https://doi.org/10.1117/12.3031915
Event: 4th International Conference on Computer Network Security and Software Engineering (CNSSE 2024), 2024, Sanya, China

Abstract

Deep learning technology has developed rapidly in recent years, and deep learning-based steganography and steganalysis techniques have also achieved fruitful results. In the past few years, the over-expanded structure of steganalyzers based on deep learning has led to huge computational and storage costs. In this article, we propose image steganalysis based on model compression, and apply the model compression method to image steganalysis to reduce the network infrastructure of the existing large-scale over-parameter steganalyzer based on deep learning. We conducted extensive experiments on the BOSSBase+BOWS2 dataset. As can be seen from the experiment, compared with the original steganalysis model, the model structure we proposed can achieve performance with fewer parameters and floating-point operations. This model has better portability and scalability.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Siyuan Huang, Minqing Zhang, Xiong Zhang, Chao Jiang, Yongjun Kong, Fuqiang Di, and Yan Ke "Image steganalysis based on model compression", Proc. SPIE 13175, International Conference on Computer Network Security and Software Engineering (CNSSE 2024), 1317517 (6 June 2024); https://doi.org/10.1117/12.3031915

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KEYWORDS

Steganalysis

Image compression

Deep learning

Education and training

Network architectures

Steganography

Performance modeling

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