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Computer Science > Computer Vision and Pattern Recognition

arXiv:2203.06691 (cs)
[Submitted on 13 Mar 2022 (v1), last revised 19 Apr 2022 (this version, v3)]

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

Authors:Naser Damer, César Augusto Fontanillo López, Meiling Fang, Noémie Spiller, Minh Vu Pham, Fadi Boutros
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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.
Comments: Accepted at CVPR Workshops 2022
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2203.06691 [cs.CV]
  (or arXiv:2203.06691v3 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2203.06691

Submission history

From: Naser Damer [view email]
[v1] Sun, 13 Mar 2022 15:55:00 UTC (18,642 KB)
[v2] Wed, 16 Mar 2022 11:28:25 UTC (18,641 KB)
[v3] Tue, 19 Apr 2022 19:49:51 UTC (18,648 KB)
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