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Robust JPEG steganography based on DCT and SVD in nonsubsampled shearlet transform domain

  • 1213: Computational Optimization and Applications for Heterogeneous Multimedia Data
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Abstract

Social media platform such as WeChat provides rich cover images for covert communication by steganography. However, in order to save band-width, storage space and make images load faster, the images often will be compressed, which makes the image steganography algorithms designed for lossless network channels unusable. Based on DCT and SVD in nonsubsampled shearlet transform domain, a robust JPEG steganography algorithm is proposed, which can resist image compression and correctly extract the embedded secret message from the compressed stego image. First, by combining the advantages of nonsubsampled shearlet transform, DCT and SVD, the construction method for robust embedding domain is proposed. Then, based on minimal distortion principle, the framework of the proposed robust JPEG steganography algorithm is given and the key steps are described in details. The experimental results show that the proposed JPEG steganography algorithm can achieve competitive robustness and anti-detection capability in contrast to the state-of-the-art robust steganography algorithms. Moreover, it can extract the secret message correctly even if the stego image is compressed by WeChat.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 61872448, U1804263) and the Natural Science Basic Research Plan in Shanxi Province of China (No. 2021JQ-379).

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Authors and Affiliations

  1. National University of Defense Technology, Xi’an, 710106, China

    Xiaofeng Song & Kun Han

  2. Zhengzhou Science and Technology Institute, Zhengzhou, 450001, China

    Chunfang Yang & Shichang Ding

  3. University of Göttingen, Göttingen, Germany

    Shichang Ding

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  1. Xiaofeng Song
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  2. Chunfang Yang
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  3. Kun Han
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  4. Shichang Ding
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Correspondence to Xiaofeng Song.

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Song, X., Yang, C., Han, K. et al. Robust JPEG steganography based on DCT and SVD in nonsubsampled shearlet transform domain. Multimed Tools Appl 81, 36453–36472 (2022). https://doi.org/10.1007/s11042-022-13525-4

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  • DOI: https://doi.org/10.1007/s11042-022-13525-4

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