Statistical Correlation as a Forensic Feature to Mitigate the Cover-Source Mismatch

Statistical Correlation as a Forensic Feature to Mitigate the Cover-Source Mismatch

연구 분야: Strategies

학회: IH&MMSec '24: Proceedings of the 2024 ACM Workshop on Information Hiding and Multimedia Security

The present paper deals with the cover-source mismatch (CSM) problem in operational steganalysis. It first investigates the distribution of the noise in natural images, and shows how this property can be used to build a fingerprint of the cover- source, to address the issue of source identification from a single image. In particular, fingerprints from different noise extraction techniques are studied. Results show that these fingerprints can be complementary. The method proposed in the present paper aggregates them in a unique forensic feature to build a more accurate source identification algorithm than when using steganalysis features, such as the discrete cosine transform residual (DCTR). Last, the paper exploits the proposed forensic tool to mitigate CSM via "atomistic steganalysis". Used together with steganalysis methods, experimental results highlight the superiority of our approach, as compared to other atomistic mitigation strategies. The relevancy of these results is further studied on out- of-camera images coming from Flickr and the ALASKA dataset. We show that for some devices, our approach gives results superior to the omniscient scenario.