PhygitalNet: Unified Face Presentation Attack Detection via One-Class Isolation Learning

Face biometric systems are shown to be vulnerable to various kinds of presentation attacks including physical and digital attacks. Existing research generally focuses on individual attacks and very few focus on generalizability across digital and physical attacks. In this research, we propose PhygitalNet model that generalizes to both physical and digital presentation attacks on face biometric systems. The proposed model is based on novel one-class iSOLatiOn Learning (SOLO Learning) which is a two-step training process aimed at reducing of the covariate shift between the bonafide samples of the physical as well as digital attack dataset in the pre-training step. In the downstream step, the algorithm introduces a novel single-class iSOLatiOn loss (SOLO loss) function that isolates the samples belonging to the bonafide class away from the samples of the attacked class for both the attack methods. Experimental results show that PhygitalNet achieves a significant performance gain when compared with the baseline techniques, evaluated on a combination of MLFP, MSU-MFSD dataset (for physical attack) and FaceForensics++ (for digital attack) datasets.