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Compressive sensing based secret signals recovery for effective image Steganalysis in secure communications

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Abstract

Conventional image steganalysis mainly focus on presence detection rather than the recovery of the original secret messages that were embedded in the host image. To address this issue, we propose an image steganalysis method featured in the compressive sensing (CS) domain, where block CS measurement matrix senses the transform coefficients of stego-image to reflect the statistical differences between the cover and stego- images. With multi-hypothesis prediction in the CS domain, the reconstruction of hidden signals is achieved efficiently. Extensive experiments have been carried out on five diverse image databases and benchmarked with four typical stegographic algorithms. The comprehensive results have demonstrated the efficacy of the proposed approach as a universal scheme for effective detection of stegography in secure communications whilst it has greatly reduced the numbers of features requested for secret signal reconstruction.

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Acknowledgements

This work was partly supported by the National Natural Science Foundation of China (61672008, 61772144), Guangdong Provincial Application-oriented Technical Research and Development Special fund project (2016B010127006, 2017A050501039), the Natural Science Foundation of Guangdong Province (2016A030311013, 2015A030313672), International Scientific and Technological Cooperation Projects of Education Department of Guangdong Province (2015KGJHZ021), and the Scientific and Technological Projects of Guangdong Province (2017A050501039).

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Authors and Affiliations

  1. School of Computer Sciences, Guangdong Polytechnic Normal University (GPNU), Guangzhou, China

    Huimin Zhao, Jin Zhan, Yinyin Xiao, Fangyuan Lei & Chunying Li

  2. The Guangzhou Key Laboratory of Digital Content Processing and Security Technologies, Guangzhou, China

    Huimin Zhao, J. -C. Ren & Sophia Y. Zhao

  3. Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, UK

    J. -C. Ren & Fangyuan Lei

  4. College of Engineering, Ajman University, Dubai, UAE

    Maher Assaad

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  1. Huimin Zhao
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  2. J. -C. Ren
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  3. Jin Zhan
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  4. Yinyin Xiao
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  5. Sophia Y. Zhao
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  6. Fangyuan Lei
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  7. Maher Assaad
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  8. Chunying Li
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Correspondence to Huimin Zhao.

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Zhao, H., Ren, JC., Zhan, J. et al. Compressive sensing based secret signals recovery for effective image Steganalysis in secure communications. Multimed Tools Appl 78, 29381–29394 (2019). https://doi.org/10.1007/s11042-018-6065-7

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  • DOI: https://doi.org/10.1007/s11042-018-6065-7

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