Abstract
JPEG steganography leverages discrete cosine transform (DCT) coefficients to embed secret messages, offering a practical medium for covert communication. However, the sparsity and block-based structure of DCT coefficients, combined with advances in steganalysis, pose significant challenges to feature extraction and anti-detection performance. To address these limitations, we propose AJSEAD, an adaptive JPEG steganographic framework that automatically learns embedding probabilities via a generative adversarial network (GAN). AJSEAD optimizes adversarial training for the unique characteristics of the DCT domain, thus providing a practical solution for secure and covert message embedding. Specifically, AJSEAD incorporates a U-shaped generator enhanced with dilated and large-kernel convolutions to improve feature extraction and employs upsampling layers to optimize decoding. For adversarial training, a dual-input steganalyzer enhances detection accuracy by jointly analyzing the spatial and DCT domains. Moreover, a refined loss function enhances AJSEAD’s resistance to detection. Extensive experiments verify the enhanced anti-detection capability and practicality of our proposed framework, demonstrating that it outperforms existing steganographic methods with various embedding strategies.
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Acknowledgements
The authors gratefully appreciate the anonymous reviewers for their insightful comments and suggestions.
Funding
This research is supported by the National Natural Science Foundation of China (Nos. 62171114 and 62032013), the Fundamental Research Funds for the Central Universities (Nos. N2424010-18 and N25LPY007), and Liaoning Provincial Science and Technology Plan Project (2023JH2/101700370).
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Yuxiang Peng contributed to conceptualization, methodology, software, original manuscript preparation, and manuscript revisions. Chong Fu supervised the research, contributed to conceptualization and methodology, and provided overall guidance. Yu Zheng and Yunjia Tian contributed to software, validation, and manuscript revisions. Guixing Cao and Junxin Chen contributed to validation and manuscript revisions. All authors reviewed and approved the final manuscript.
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Peng, Y., Fu, C., Zheng, Y. et al. AJSEAD: Adaptive JPEG steganography with enhanced anti-detection via generative adversarial network. SIViP 19, 1255 (2025). https://doi.org/10.1007/s11760-025-04780-7
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DOI: https://doi.org/10.1007/s11760-025-04780-7
