Countering anti-forensics by means of data fusion

In the last years many image forensic (IF) algorithms have been proposed to reveal traces of processing or tampering. On the other hand, Anti-Forensic (AF) tools have also been developed to help the forger in removing editing footprints. Inspired by the fact that it is much harder to commit a perfect crime when the forensic analyst uses a multi-clue investigation strategy, we analyse the possibility offered by the adoption of a data fusion framework in a Counter-Anti-Forensic (CAF) scenario. We do so by adopting a theoretical framework, based on Dempster-Shafer Theory of Evidence, to synergically merge information provided by IF tools and CAF tools, whose goal is to reveal traces introduced by anti-forensic algorithms. The proposed system accounts for the non-trivial relationships between IF and CAF techniques; for example, in some cases the outputs from the former are expected to contradict the output from the latter. We evaluate the proposed method within a representative forensic task, that is splicing detection in JPEG images, with the forger trying to conceal traces using two different counter-forensic methods. Results show that decision fusion strongly limits the effectiveness of AF methods.