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Enhancing reliability and efficiency for real-time robust adaptive steganography using cyclic redundancy check codes

Abstract

The development of multimedia and deep learning technology bring new challenges to steganography and steganalysis techniques. Meanwhile, robust steganography, as a class of new techniques aiming to solve the problem of covert communication under lossy channels, has become a new research hotspot in the field of information hiding. To improve the communication reliability and efficiency for current real-time robust steganography methods, a concatenated code, composed of Syndrome–Trellis codes (STC) and cyclic redundancy check (CRC) codes, is proposed in this paper. The enhanced robust adaptive steganography framework proposed is this paper is characterized by a strong error detection capability, high coding efficiency, and low embedding costs. On this basis, three adaptive steganographic methods resisting JPEG compression and detection are proposed. Then, the fault tolerance of the proposed steganography methods is analyzed using the residual model of JPEG compression, thus obtaining the appropriate coding parameters. Experimental results show that the proposed methods have a significantly stronger robustness against compression, and are more difficult to be detected by statistical based steganalytic methods.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (NSFC nos. U1804263, U1736214, U1636219, 61872448, 61772549, and 61602508), the National Key R&D Program (nos. 2016YFB0801303, 2016QY01W0105), and the Science and Technology Innovation Talent Project of Henan Province (no. 184200510018).

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Correspondence to Xiangyang Luo.

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Zhang, Y., Luo, X., Zhu, X. et al. Enhancing reliability and efficiency for real-time robust adaptive steganography using cyclic redundancy check codes. J Real-Time Image Proc 17, 115–123 (2020). https://doi.org/10.1007/s11554-019-00905-7

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Keywords

  • Robust steganography
  • STC–CRC codes
  • JPEG compression resistant
  • Statistical detection resistant