PRNU-based forgery detection with regularity constraints and global optimization
Detection of image forgeries is an important topic for forensics applications. One of the most interesting approaches to forgery detection relies on the photo-response non uniformity noise (PRNU), that can be considered as a sort of camera fingerprint and used as such to accomplish forgery detection. In fact, while genuine parts of an image exhibit the camera PRNU, this is not present in tampered areas. In this work, we present a new method to detect forgeries by using PRNU. In particular, we propose a minimum-risk Bayesian classification, aimed at minimizing the probability of error or, more in general, a weighted average of the two types of errors (false alarm, missing detection) according to their importance for the application. Then, we introduce a regularization term in the decision process to take into account prior information on the classification map. This step weights optimally the observed data and the regularity constraints to minimize the Bayesian risk. Since the regularization term is based on spatial properties of the decision map, classification cannot work on each pixel individually but must be carried out jointly on the whole image. The ensuing problem is NP-hard but we use relaxation and convex optimization techniques, based on proximal methods, to obtain a global optimum solution in limited time. Preliminary experiments with digital forgeries of different sizes and shapes prove that the improved technique provides a significant performance gain w.r.t. the original, at the cost of a limited increase in complexity.