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  4. PatchSwap: Boosting the Generalizability of Face Presentation Attack Detection by Identity-aware Patch Swapping
 
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2022
Conference Paper
Titel

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

Abstract
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.
Author(s)
Fang, Meiling
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Hamza, Ali
TU Darmstadt, Fachgebiet Graphisch-Interaktive Systeme
Kuijper, Arjan orcid-logo
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Damer, Naser
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Hauptwerk
IEEE International Joint Conference on Biometrics, IJCB 2022
Project(s)
Next Generation Biometric Systems
Next Generation Biometric Systems
Funder
Bundesministerium für Bildung und Forschung -BMBF-
Hessisches Ministerium für Wissenschaft und Kunst
Konferenz
International Joint Conference on Biometrics 2022
Thumbnail Image
DOI
10.1109/IJCB54206.2022.10007946
Language
English
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Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Tags
  • Branche: Information Technology

  • Research Line: Computer vision (CV)

  • Research Line: Machine learning (ML)

  • LTA: Scalable architectures for massive data sets

  • Biometrics

  • Machine learning

  • Deep learning

  • Face recognition

  • Attack detection

  • ATHENE

  • CRISP

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