From blind to quantitative steganalysis

Tomáš Pevny; Jessica Fridrich; Andrew D. Ker

doi:10.1117/12.805601

4 February 2009 From blind to quantitative steganalysis

Tomáš Pevny, Jessica Fridrich, Andrew D. Ker

Proceedings Volume 7254, Media Forensics and Security; 72540C (2009) https://doi.org/10.1117/12.805601
Event: IS&T/SPIE Electronic Imaging, 2009, San Jose, California, United States

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.

Citation Download Citation

Tomáš Pevny, Jessica Fridrich, and Andrew D. Ker "From blind to quantitative steganalysis", Proc. SPIE 7254, Media Forensics and Security, 72540C (4 February 2009); https://doi.org/10.1117/12.805601

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