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  4. PW-MAD: Pixel-Wise Supervision for Generalized Face Morphing Attack Detection
 
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2021
Conference Paper
Title

PW-MAD: Pixel-Wise Supervision for Generalized Face Morphing Attack Detection

Abstract
A face morphing attack image can be verified to multiple identities, making this attack a major vulnerability to processes based on identity verification, such as border checks. Various methods have been proposed to detect face morphing attacks, however, with low generalizability to unexpected post-morphing processes. A major post-morphing process is the print and scan operation performed in many countries when issuing a passport or identity document. In this work, we address this generalization problem by adapting a pixel-wise supervision approach where we train a network to classify each pixel of the image into an attack or not, rather than only having one label for the whole image. Our pixel-wise morphing attack detection (PW-MAD) solution proved to perform more accurately than a set of established baselines. More importantly, PW-MAD shows high generalizability in comparison to related works, when evaluated on unknown re-digitized attacks. Additionally to our PW-MAD approach, we create a new face morphing attack dataset with digital and re-digitized samples, namely the LMA-DRD dataset that is publicly available for research purposes upon request.
Author(s)
Damer, Naser  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Spiller, Noémie Catherine Hélène
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Fang, Meiling  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Boutros, Fadi  orcid-logo
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Kirchbuchner, Florian  orcid-logo
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Kuijper, Arjan  orcid-logo
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Mainwork
Advances in Visual Computing. 16th International Symposium, ISVC 2021. Proceedings. Pt.I  
Project(s)
ATHENE
Funder
Bundesministerium für Bildung und Forschung BMBF (Deutschland)  
Conference
International Symposium on Visual Computing (ISVC) 2021  
DOI
10.1007/978-3-030-90439-5_23
Language
English
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Keyword(s)
  • Lead Topic: Smart City

  • Lead Topic: Visual Computing as a Service

  • Research Line: Computer vision (CV)

  • Research Line: Human computer interaction (HCI)

  • Research Line: Machine Learning (ML)

  • biometrics

  • face recognition

  • Morphing Attack

  • deep learning

  • machine learning

  • CRISP

  • ATHENE

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