Estimating steganographic fisher information in real images

Abstract

This paper is concerned with the estimation of steganographic capacity in digital images, using information theoretic bounds and very large-scale experiments to approximate the distributions of genuine covers. The complete distribution cannot be estimated, but with carefully-chosen algorithms and a large corpus we can make local approximations by considering groups of pixels. A simple estimator for the local quadratic term of Kullback-Leibler divergence (Steganographic Fisher Information) is presented, validated on some synthetic images, and computed for a corpus of covers. The results are interesting not so much for their concrete capacity estimates but for the comparisons they provide between different embedding operations, between the information found in differently-sized and -shaped pixel groups, and the results of DC normalization within pixel groups. This work suggests lessons for the future design of spatial-domain steganalysis, and also the optimization of embedding functions. © 2009 Springer Berlin Heidelberg.