The challenges of rich features in universal steganalysis

Contemporary steganalysis is driven by new steganographic rich feature sets, which consist of large numbers of weak features. Although extremely powerful when applied to supervised classification problems, they are not compatible with unsupervised universal steganalysis, because the unsupervised method cannot separate the signal (evidence of steganographic embedding) from the noise (cover content). This work tries to alleviate the problem, by means of feature extraction algorithms. We focus on linear projections informed by embedding methods, and propose a new method which we call calibrated least squares with the specific aim of making the projections sensitive to stego content yet insensitive to cover variation. Different projections are evaluated by their application to the anomaly detector from Ref. 1, and we are able to retain both the universality and the robustness of the method, while increasing its performance substantially. © 2013 SPIE-IS&T.