Publikationen

Autor	Fang, Meiling; Hamza, Ali; Kuijper, Arjan; Damer, Naser
Datum	2022
Art	Conference Paper
Abstrakt	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.
Konferenz	International Joint Conference on Biometrics 2022
ISBN	978-1-6654-6394-2
Publisher	IEEE
Projekt	Next Generation Biometric Systems
Url	https://publica.fraunhofer.de/handle/publica/435321

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