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

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

On Completeness of Feature Spaces in Blind Steganalysis

Blind steganalyzers can be used for many diverse applications in steganography that go well beyond a mere detection of stego content. A blind steganalyzer can also be used for constructing targeted attacks or as an oracle for designing steganographic methods. The feature space itself provides a low-dimensional model of covers useful for benchmarking. These applications require the feature space to be complete in the sense that the features fully characterize the space of covers. Incomplete feature sets may skew benchmarking scores and lead to poor steganalysis. As a simple test of completeness, we propose a general approach for constructing steganographic methods that approximately preserve the whole feature vector and thus become practically undetectable by any steganalyzer that uses the same feature set. We demonstrate the plausibility of this approach, which we call the Feature Correction Method (FCM) by constructing the FCM for a 274-dimensional feature set from a state-of-the-art blind steganalyzer for JPEG images