  Despite the impressive progress in the field of presentation attack detection
and multimedia forensics over the last decade, these systems are still
vulnerable to attacks in real-life settings. Some of the challenges for
existing solutions are the detection of unknown attacks, the ability to perform
in adversarial settings, few-shot learning, and explainability. In this study,
these limitations are approached by reliance on a game-theoretic view for
modeling the interactions between the attacker and the detector. Consequently,
a new optimization criterion is proposed and a set of requirements are defined
for improving the performance of these systems in real-life settings.
Furthermore, a novel detection technique is proposed using generator-based
feature sets that are not biased towards any specific attack species. To
further optimize the performance on known attacks, a new loss function coined
categorical margin maximization loss (C-marmax) is proposed which gradually
improves the performance against the most powerful attack. The proposed
approach provides a more balanced performance across known and unknown attacks
and achieves state-of-the-art performance in known and unknown attack detection
cases against rational attackers. Lastly, the few-shot learning potential of
the proposed approach is studied as well as its ability to provide pixel-level
explainability.
