Image robust adaptive steganography adapted to lossy channels in open social networks

Information Sciences(2021)

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Currently, the demand for covert communication in open social networks brings new opportunities and challenges to existing image steganography technology in terms of robustness and security. To this end, an image robust adaptive steganography is proposed with robustness against multiple image processing attacks and detection resistance. First, a robust embedding domain with theoretical foundation and optimal invisibility is constructed based on the compression resistance principle. Then, utilizing the robust image abstraction and saliency measurement, the embedding channel is selected to avoid modifications in smooth regions and enhance visual quality. On this basis, the proposed method is given combining with error-correcting and STC codes to realize message embedding with minimum costs and improve extraction accuracy. Lastly, after parameters discussion and selection, the performance experiments are conducted compared with previous representative steganography algorithms, concerning robustness and detection resistance, and the fault tolerance is deduced, thereby providing the recommended coding parameters to improve message extraction integrity. The experimental results show that the proposed method can realize message extraction with high accuracy after JPEG compression, Gaussian noising, and scaling attacks, while holding comparable detection resistance to adaptive steganography against statistical features, which indicates its application prospect for covert communication in open lossy channels.
Robust adaptive steganography,Open lossy channels,Statistical detection,Robust domain construction,Embedding channel selection
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