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Proceedings Paper

Random projections of residuals as an alternative to co-occurrences in steganalysis
Author(s): Vojtěch Holub; Jessica Fridrich; Tomáš Denemark
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Paper Abstract

Today, the most reliable detectors of steganography in empirical cover sources, such as digital images coming from a known source, are built using machine-learning by representing images with joint distributions (co-occurrences) of neighboring noise residual samples computed using local pixel predictors. In this paper, we propose an alternative statistical description of residuals by binning their random projections on local neighborhoods. The size and shape of the neighborhoods allow the steganalyst to further diversify the statistical description and thus improve detection accuracy, especially for highly adaptive steganography. Other key advantages of this approach include the possibility to model long-range dependencies among pixels and making use of information that was previously underutilized in the marginals of co-occurrences. Moreover, the proposed approach is much more flexible than the previously proposed spatial rich model, allowing the steganalyst to obtain a significantly better trade off between detection accuracy and feature dimensionality. We call the new image representation the Projection Spatial Rich Model (PSRM) and demonstrate its effectiveness on HUGO and WOW – two current state-of-the-art spatial-domain embedding schemes.

Paper Details

Date Published: 22 March 2013
PDF: 11 pages
Proc. SPIE 8665, Media Watermarking, Security, and Forensics 2013, 86650L (22 March 2013); doi: 10.1117/12.1000330
Show Author Affiliations
Vojtěch Holub, Binghamton Univ., SUNY (United States)
Jessica Fridrich, Binghamton Univ., SUNY (United States)
Tomáš Denemark, Binghamton Univ., SUNY (United States)

Published in SPIE Proceedings Vol. 8665:
Media Watermarking, Security, and Forensics 2013
Adnan M. Alattar; Nasir D. Memon; Chad D. Heitzenrater, Editor(s)

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