The goal of PREMIER is to overcome the above shortcomings and develop a new class of AI-based MMF tools, that can be successfully used to preserve the dependability of digital media. To do so, PREMIER will pursue a novel approach whereby AI-techniques are enriched with a model-based signal-processing view point. In this way, the strengths of data-driven techniques will be maintained and any available information stemming from the scenario at hand exploited. In this vein, the need for huge amount of training data will be relaxed by constraining the network structure and the training process so to orient the analysis towards task-relevant features.

The strong connection between certain classes of constrained CNN and signal processing methodologies will be exploited to ease the interpretability of the forensic analysis. The use of signal-processing-oriented structures, in fact, eases the construction of temporal and spatial heat-maps showing which parts of the analyzed media contributed most to the detector’s outcome. A better interpretation of the analysis will also ensure that MMF detectors base their decisions on task-relevant features avoiding so-called confounding factors, which can be easily attacked by a forger. Still regarding security, the joint use of self-learned and handcrafted features will be exploited to improve the resilience of the detectors against deliberate attacks.

The project will mainly focus on video forensics, due to the importance that video has in the formation of opinions and the diffusion of information, and because research in video forensics is much less advanced than for still images, and hence represents a more challenging area wherein the soundness of PREMIER viewpoint can be verified.