A fuzzy approach to deal with uncertainty in image forensics

Image forensics research has mainly focused on the detection of artifacts introduced by a single processing tool, thus resulting in the development of a large number of specialized algorithms looking for one or more specific footprints under precise settings. As one may guess, the performance of such algorithms are not ideal, so the output they provide may be noisy, inaccurate and only partially true. Moreover, in real scenarios a manipulated image is often the result of the application of several tools made available by the image processing software. As a consequence, reliable tamper detection requires that several tools developed to deal with different scenarios are applied. The above observations raise two new problems: (i) deal with the uncertainty introduced by error-prone tools and (ii) devise a sound strategy to merge the information provided by the different tools into a single output. To overcome these problems we propose a decision fusion framework based on the Fuzzy Theory, which permits to cope with the uncertainty and lack of precise information typical of imageforensics, by leveraging on the widely known ability of the Fuzzy Theory to deal with inaccurate and incomplete information. We describe a practical implementation of the proposed framework and validate it in a realistic scenario in which five forensic tools exploit JPEG compression artifacts to detect cut&paste tampering within a specified region of an image. The results are encouraging, and provide a significant advantage with respect to those obtained by simply OR-ing the outputs of the single tools.