Biblio

In this paper, we introduce a new digital image forensics approach called forensic similarity, which determines whether two image patches contain the same forensic trace or different forensic traces. One benefit of this approach is that prior knowledge, e.g., training samples, of a forensic trace is not required to make a forensic similarity decision on it in the future. To do this, we propose a two-part deep-learning system composed of a convolutional neural network-based feature extractor and a three-layer neural network, called the similarity network. This system maps the pairs of image patches to a score indicating whether they contain the same or different forensic traces. We evaluated the system accuracy of determining whether two image patches were captured by the same or different camera model and manipulated by the same or a different editing operation and the same or a different manipulation parameter, given a particular editing operation. Experiments demonstrate applicability to a variety of forensic traces and importantly show efficacy on “unknown” forensic traces that were not used to train the system. Experiments also show that the proposed system significantly improves upon prior art, reducing error rates by more than half. Furthermore, we demonstrated the utility of the forensic similarity approach in two practical applications: forgery detection and localization, and database consistency verification.

In recent decades, a significant research effort has been devoted to the development of forensic tools for retrieving information and detecting possible tampering of multimedia documents. A number of counter-forensic tools have been developed as well in order to impede a correct analysis. Such tools are often very effective due to the vulnerability of multimedia forensics tools, which are not designed to work in an adversarial environment. In this scenario, developing forensic techniques capable of granting good performance even in the presence of an adversary aiming at impeding the forensic analysis, is becoming a necessity. This turns out to be a difficult task, given the weakness of the traces the forensic analysis usually relies on. The goal of this paper is to provide an overview of the advances made over the last decade in the field of adversarial multimedia forensics. We first consider the view points of the forensic analyst and the attacker independently, then we review some of the attempts made to simultaneously take into account both perspectives by resorting to game theory. Eventually, we discuss the hottest open problems and outline possible paths for future research.