Countering Anti-Forensics by Means of Data Fusion

摘要

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.