Modeling and Extending the Ensemble Classifier for Steganalysis of Digital Images Using Hypothesis Testing Theory

The machine learning paradigm currently predominantly used for steganalysis of digital images works on the principle of fusing the decisions of many weak base learners. In this paper, we employ a statistical model of such an ensemble and replace the majority voting rule with a likelihood ratio test. This allows us to train the ensemble to guarantee desired statistical properties, such as the false-alarm probability and the detection power, while preserving the high detection accuracy of original ensemble classifier. It also turns out the proposed test is linear. Moreover, by replacing the conventional total probability of error with an alternative criterion of optimality, the ensemble can be extended to detect messages of an unknown length to address composite hypotheses. Finally, the proposed well-founded statistical formulation allows us to extend the ensemble to multi-class classification with an appropriate criterion of optimality and an optimal associated decision rule. This is useful when a digital image is tested for the presence of secret data hidden by more than one steganographic method. Numerical results on real images show the sharpness of the theoretically established results and the relevance of the proposed methodology.