Paper Info
Title
Compressive sensing based secret signals recovery for effective image Steganalysis in secure communications
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.
Year
DOI
Venue
2019
10.1007/s11042-018-6065-7
Multimedia Tools and Applications
Keywords
Field
DocType
Compressive sensing (CS), Image steganalysis, Secret signal recovery, Secure communication
Computer vision,Steganography,Pattern recognition,Matrix (mathematics),Computer science,Artificial intelligence,Steganalysis,Compressed sensing,Signal reconstruction,Secure communication
Journal
Volume
Issue
ISSN
78.0
20
1573-7721
Citations 
PageRank 
References 
1
0.37
24
Authors
8
Name
Order
Citations
PageRank
Huimin Zhao120623.43
Jinchang Ren2114488.54
Jin Zhan3393.57
Yinyin Xiao441.79
Sophia Y. Zhao510.37
Fangyuan Lei640.78
Maher Assaad742.49
Chunying Li842.49