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State-of-the-art spoof detection methods tend to overfit to the spoof typesseen during training and fail to generalize to unknown spoof types. Given thatface anti-spoofing is inherently a local task, we propose a face anti-spoofingframework, namely Self-Supervised Regional Fully Convolutional Network(SSR-FCN), that is trained to learn local discriminative cues from a face imagein a self-supervised manner. The proposed framework improves generalizabilitywhile maintaining the computational efficiency of holistic face anti-spoofingapproaches (< 4 ms on a Nvidia GTX 1080Ti GPU). The proposed method isinterpretable since it localizes which parts of the face are labeled as spoofs.Experimental results show that SSR-FCN can achieve TDR = 65% @ 2.0% FDR whenevaluated on a dataset comprising of 13 different spoof types under unknownattacks while achieving competitive performances under standard benchmarkdatasets (Oulu-NPU, CASIA-MFSD, and Replay-Attack).

Look Locally Infer Globally: A Generalizable Face Anti-Spoofing Approach