Selection of robust features for the Cover Source Mismatch problem in 3D steganalysis

This paper introduces a novel method for extracting sets of feature from 3D objects characterising a robust steganalyzer. Specifically, the proposed steganalyzer should mitigate the Cover Source Mismatch (CSM) paradigm. A steganalyzer is considered as a classifier aiming to identify separately cover and stego objects. A steganalyzer behaves as a classifier by considering a set of features extracted from cover stego pairs of 3D objects as inputs during the training stage. However, during the testing stage, the steganalyzer would have to identify whether specific information was hidden in a set of 3D objects which can be different from those used during the training. Addressing the CSM paradigm corresponds to testing the generalization ability of the steganalyzer when introducing distortions in the cover objects before hiding information through steganography. Our method aims to select those 3D features that model best the changes introduced in objects by steganography or information hiding and moreover they are able to generalize for different objects, not present in the training set. The proposed robust steganalysis approach is tested when considering changes in 3D objects such as those produced by mesh simplification and additive noise. The results obtained from this study show that the steganalyzers trained with the selected set of robust features achieve better detection accuracy of the changes embedded in the objects, when compared to other sets of features.