Robust image steganography against lossy JPEG compression based on embedding domain selection and adaptive error correction
Expert Systems with Applications, ISSN: 0957-4174, Vol: 229, Page: 120416
2023
- 13Citations
- 14Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Article Description
Transmitting images for communication on social networks has become routine, which is helpful for covert communication. The traditional steganography algorithm is unable to successfully convey secret information since the social network channel will perform lossy operations on images, such as JPEG compression. Previous studies tried to solve this problem by enhancing the robustness or making the cover adapt to the channel processing. In this study, we proposed a robust image steganography method against lossy JPEG compression based on embedding domain selection and adaptive error correction. To improve anti-steganalysis performance, the embedding domain is selected adaptively. To increase robustness and lessen the impact on anti-steganalysis performance, the error correction capacity of the error correction code is adaptively adjusted to eliminate redundancy. The experimental results show that the proposed method achieves better anti-steganalysis and robustness.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0957417423009181; http://dx.doi.org/10.1016/j.eswa.2023.120416; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85160210833&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0957417423009181; https://dx.doi.org/10.1016/j.eswa.2023.120416
Elsevier BV
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