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

Tools

Zhao, Huimin and Ren, J. -C. and Zhan, Jin and Xiao, Yinyin and Zhao, Sophia Y. and Lei, Fangyuan and Assaad, Maher and Li, Chunying (2018) Compressive sensing based secret signals recovery for effective image steganalysis in secure communications. Multimedia Tools and Applications. ISSN 1380-7501 (https://doi.org/10.1007/s11042-018-6065-7)

[thumbnail of Zhao-etal-MTA-2018-Compressive-sensing-based-secret-signals-recovery]
Preview
 Text. Filename: Zhao_etal_MTA_2018_Compressive_sensing_based_secret_signals_recovery.pdf
Accepted Author Manuscript
Download (858kB)| Preview

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.

ORCID iDs

Zhao, Huimin, Ren, J. -C. ORCID logoORCID: https://orcid.org/0000-0001-6116-3194, Zhan, Jin, Xiao, Yinyin, Zhao, Sophia Y., Lei, Fangyuan, Assaad, Maher and Li, Chunying;
CORE (COnnecting REpositories)