22 March 2013 Quantitative steganalysis using rich models
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Abstract
In this paper, we propose a regression framework for steganalysis of digital images that utilizes the recently proposed rich models – high-dimensional statistical image descriptors that have been shown to substantially improve classical (binary) steganalysis. Our proposed system is based on gradient boosting and utilizes a steganalysis-specific variant of regression trees as base learners. The conducted experiments confirm that the proposed system outperforms prior quantitative steganalysis (both structural and feature-based) across a wide range of steganographic schemes: HUGO, LSB replacement, nsF5, BCHopt, and MME3.
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Jan Kodovský, Jessica Fridrich, "Quantitative steganalysis using rich models", Proc. SPIE 8665, Media Watermarking, Security, and Forensics 2013, 86650O (22 March 2013); doi: 10.1117/12.2001563; https://doi.org/10.1117/12.2001563
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