4 February 2009 From blind to quantitative steganalysis
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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.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Tomáš Pevny, Tomáš Pevny, Jessica Fridrich, Jessica Fridrich, Andrew D. Ker, Andrew D. Ker, } "From blind to quantitative steganalysis", Proc. SPIE 7254, Media Forensics and Security, 72540C (4 February 2009); doi: 10.1117/12.805601; https://doi.org/10.1117/12.805601


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