Quantitative steganalysis using rich models

Jan Kodovský; Jessica Fridrich

doi:10.1117/12.2001563

22 March 2013 Quantitative steganalysis using rich models

Jan Kodovský, Jessica Fridrich

Proceedings Volume 8665, Media Watermarking, Security, and Forensics 2013; 86650O (2013) https://doi.org/10.1117/12.2001563
Event: IS&T/SPIE Electronic Imaging, 2013, Burlingame, California, United States

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.

Citation Download Citation

Jan Kodovský and Jessica Fridrich "Quantitative steganalysis using rich models", Proc. SPIE 8665, Media Watermarking, Security, and Forensics 2013, 86650O (22 March 2013); https://doi.org/10.1117/12.2001563

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