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Proceedings Paper

From blind to quantitative steganalysis
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Paper Abstract

Quantitative steganalyzers are important in forensic steganalysis as they can estimate the payload, or, more precisely, the number of embedding changes in the stego image. This paper proposes a general method for constructing quantitative steganalyzers from features used in blind detectors. The method is based on support vector regression, which is used to learn the mapping between a feature vector extracted from the image and the relative embedding change rate. The performance is evaluated by constructing quantitative steganalyzers for eight steganographic methods for JPEG files, using a 275-dimensional feature set. Error distributions of within- and between-image errors are empirically estimated for Jsteg and nsF5. For Jsteg, the accuracy is compared to state-of-the-art quantitative steganalyzers.

Paper Details

Date Published: 4 February 2009
PDF: 14 pages
Proc. SPIE 7254, Media Forensics and Security, 72540C (4 February 2009); doi: 10.1117/12.805601
Show Author Affiliations
Tomáš Pevny, INPG, Gipsa-Lab (France)
Jessica Fridrich, Binghamton Univ., SUNY (United States)
Andrew D. Ker, Oxford Univ. (United Kingdom)

Published in SPIE Proceedings Vol. 7254:
Media Forensics and Security
Edward J. Delp; Jana Dittmann; Nasir D. Memon; Ping Wah Wong, Editor(s)

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