PatchSwap: Boosting the Generalizability of Face Presentation Attack Detection by Identity-aware Patch Swapping

Face presentation attack detection (PAD) is essential in mitigating spoofing attack vulnerabilities in face recognition systems. Despite the relatively good detection performance of PADs on known attacks, they tend to be challenged by unknown samples. To address this issue, we present our PatchSwap approach that aims at creating more challenging and complex bona fide, attack, and partial attack samples despite limited training resources. The PatchSwap operates by swapping intra-identity patches between training samples and correspondingly updates their pixel-wise mask label, all under a controlled strategy. The PatchSwap is deployed as an augmentation technique and can be effortlessly integrated into any model training process. The different choices towards our PatchSwap design are exhaustively investigated and proven in detailed studies. We conduct extensive experiments under intra-dataset and cross-dataset scenarios and on three different network backbones. The experimental results showed that the PatchSwap successfully induces significant gains in the PAD performance under different evaluation settings.