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

Huimin Zhao, J. -C. Ren, Jin Zhan, Yinyin Xiao, Sophia Y. Zhao, Fangyuan Lei, Maher Assaad, Chunying Li

Research output: Contribution to journalSpecial issuepeer-review

2 Citations (Scopus)
33 Downloads (Pure)


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.
Original languageEnglish
Number of pages14
JournalMultimedia Tools and Applications
Early online date23 May 2018
Publication statusE-pub ahead of print - 23 May 2018


  • compressive sensing (CS)
  • image steganalysis
  • secret signal recovery
  • secure communication


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