4.6 Article

Robust steganography resisting JPEG compression by improving selection of cover element

期刊

SIGNAL PROCESSING
卷 183, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.sigpro.2021.108048

关键词

Robust steganography; Social network platform; JPEG compression; Sign of DCT coefficients

资金

  1. Natural Science Foundation of China [61702150, U1804263, U1636219, U1736214]
  2. Zhongyuan Science and Technology Innovation Leading Talent Project [214200510019]
  3. Public Research Project of Zhejiang Province [LGG19F020015]
  4. National Key R&D Program of China [2016YFB0801303, 2016QY01W0105]

向作者/读者索取更多资源

An enhanced robust steganographic algorithm is proposed in this paper to resist against JPEG compression by improving the selection of cover element, leading to superior robustness compared to current techniques and verified effectiveness on social network platforms.
Through minimizing embedding cost, modern adaptive steganography has gained unprecedented success in terms of its undetectability. However, due to Joint Photographic Experts Group (JPEG) compression, the secret bits hidden in the image transmitted over social network platform fail to be perfectly extracted, that remarkably limits its wide application in the real world. In this paper, by improving selection of cover element, we propose to design an enhanced robust steganographic algorithm to resist against JPEG compression. First, since that before and after JPEG compression the sign of Discrete Cosine Transform (DCT) coefficient is not easy to change, we devise cover element based on the sign of DCT coefficient. Furthermore, the selection of cover element is successfully improved with the help of post-processing operation analysis. Second, the embedding cost for each candidate DCT coefficient is calculated. Finally, dependent of both error correction algorithm and syndrome-trellis codes, a compression-resistant stego image is generated with minimum distortion. Numerical results verify that the robustness of our proposed steganographic algorithm is superior to that of current arts; meanwhile, the effectiveness of the algorithm is also verified over social network platform. (C) 2021 Elsevier B.V. All rights reserved.

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