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.
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