Facing the Cover-Source Mismatch on JPHide using Training-Set Design

This short paper investigates the influence of the image processing pipeline (IPP) on the cover-source mismatch (CSM) for the popular JPHide steganographic scheme. We propose to deal with CSM by combining a forensics and a steganalysis approach. A multi-classifier is first trained to identify the IPP, and secondly a specific training set is designed to train a targeted classifier for steganalysis purposes. We show that the forensic step is immune to the steganographic embedding. The proposed IPP-informed steganalysis outperforms classical strategies based on training on a mixture of sources and we show that it can provide results close to a detector specifically trained on the appropriate source.