Dealing with uncertainty in image forensics: A fuzzy approach

Image forensics research has mainly focused on the detection of artifacts introduced by a single processing tool. In tamper detection applications, however, the kind of artifacts the forensic analyst should look for is not known beforehand, hence making it necessary that several tools developed for different scenarios are applied. The problem, then, is twofold: i) devise a sound strategy to elaborate the information provided by the different tools into a single output, and ii) deal with the uncertainty introduced by error-prone tools. In this paper, we introduce a framework based on Fuzzy Theory to overcome these problems. We describe a practical implementation of the proposed framework putting the theoretical principles in practice. To validate the proposed approach, we carried out some experiments addressing a simple realistic scenario in which three forensic tools exploit artifacts introduced by JPEG compression to detect cut&paste tampering within a specified region of an image. The results are encouraging, especially when compared with those obtained by simply XOR-ing the output of the the single detection tools.