Unsupervised fusion for forgery localization exploiting background information

When image authenticity verification has to be carried out without any knowledge about the possible processing undergone by the image under analysis, it is fundamental to rely on a multi-clue approach, that merges the information stemming from several complementary forensic tools. This paper introduces a fully automatic framework for fusing the maps created by a set of unsupervised forgery localization algorithms, indicating possible manipulated areas. The framework takes into account the forgery maps, their reliability and the compatibility among the different traces considered by the tools. The achieved localization map is then refined by exploiting image content, thus improving the performance of the proposed system with respect to state of the art approaches.