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  4. Privacy-friendly Synthetic Data for the Development of Face Morphing Attack Detectors
 
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2022
Paper (Preprint, Research Paper, Review Paper, White Paper, etc.)
Title

Privacy-friendly Synthetic Data for the Development of Face Morphing Attack Detectors

Title Supplement
Preprint of the Conference Paper, Conference on Computer Vision and Pattern Recognition 2022
Abstract
The main question this work aims at answering is: ”can morphing attack detection (MAD) solutions be successfully developed based on synthetic data?”. Towards that, this work introduces the first synthetic-based MAD development dataset, namely the Synthetic Morphing Attack Detection Development dataset (SMDD). This dataset is utilized successfully to train three MAD backbones where it proved to lead to high MAD performance, even on completely unknown attack types. Additionally, an essential aspect of this work is the detailed legal analyses of the challenges of using and sharing real biometric data, rendering our proposed SMDD dataset extremely essential. The SMDD dataset, consisting of 30,000 attack and 50,000 bona fide samples, is publicly available for research purposes.
Author(s)
Damer, Naser  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
López, César Augusto Fontanillo
KU Leuven  
Fang, Meiling  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Spiller, Noemie
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Pham, Minh Vu
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Boutros, Fadi  orcid-logo
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Project(s)
Next Generation Biometric Systems  
Funder
Bundesministerium für Bildung und Forschung -BMBF-
Open Access
DOI
10.24406/publica-172
File(s)
Damer_Privacy-Friendly_Synthetic_Data_for_the_Development_of_Face_Morphing_Attack_CVPRW_2022_paper.pdf (3.06 MB)
Rights
CC BY
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: Machine Learning (ML)

  • Biometrics

  • Morphing attack

  • Face recognition

  • Machine learning

  • Deep learning

  • ATHENE

  • CRISP

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