Optimizing pixel predictors for steganalysis

Vojtech Holub; Jessica Fridrich

doi:10.1117/12.905753

9 February 2012 Optimizing pixel predictors for steganalysis

Vojtech Holub, Jessica Fridrich

Proceedings Volume 8303, Media Watermarking, Security, and Forensics 2012; 830309 (2012) https://doi.org/10.1117/12.905753
Event: IS&T/SPIE Electronic Imaging, 2012, Burlingame, California, United States

Abstract

A standard way to design steganalysis features for digital images is to choose a pixel predictor, use it to compute a noise residual, and then form joint statistics of neighboring residual samples (co-occurrence matrices). This paper proposes a general data-driven approach to optimizing predictors for steganalysis. First, a local pixel predictor is parametrized and then its parameters are determined by solving an optimization problem for a given sample of cover and stego images and a given cover source. Our research shows that predictors optimized to detect a specific case of steganography may be vastly different than predictors optimized for the cover source only. The results indicate that optimized predictors may improve steganalysis by a rather non-negligible margin. Furthermore, we construct the predictors sequentially - having optimized k predictors, design the k + 1st one with respect to the combined feature set built from all k predictors. In other words, given a feature space (image model) extend (diversify) the model in a selected direction (functional form of the predictor) in a way that maximally boosts detection accuracy.

Citation Download Citation

Vojtech Holub and Jessica Fridrich "Optimizing pixel predictors for steganalysis", Proc. SPIE 8303, Media Watermarking, Security, and Forensics 2012, 830309 (9 February 2012); https://doi.org/10.1117/12.905753

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