Multiple Classifier Systems for Image Forgery Detection

A large number of techniques have been proposed recently for forgery detection, based on widely different principles and processing tools. As a result, each technique performs well with some types of forgery, and under given hypotheses, and much worse in other situations. To improve robustness, one can merge the output of different techniques but it is not obvious how to balance the different sources of information. In this paper we consider and test several combining rules, working both at the abstract level and at measurement level, and providing information on both presence and location of suspect tampered regions. Experimental results on a suitable dataset of forged images show that a careful fusion of detector's output largely outperforms individual detectors, and that measurement-level fusion methods are more effective than abstract-level ones.