Damer, Naser ; López, César Augusto Fontanillo ; Fang, Meiling ; Spiller, Noémie ; Pham, Minh Vu ; Boutros, Fadi (2022)
Privacy-friendly Synthetic Data for the Development of Face Morphing Attack Detectors.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022). New Orleans, USA (19.-24.06.2022)
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Damer, Naser ; López, César Augusto Fontanillo ; Fang, Meiling ; Spiller, Noémie ; Pham, Minh Vu ; Boutros, Fadi |
| Art des Eintrags: | Bibliographie |
| Titel: | Privacy-friendly Synthetic Data for the Development of Face Morphing Attack Detectors |
| Sprache: | Englisch |
| Publikationsjahr: | 15 August 2022 |
| Veranstaltungstitel: | IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022) |
| Veranstaltungsort: | New Orleans, USA |
| Veranstaltungsdatum: | 19.-24.06.2022 |
| Zugehörige Links: | |
| Kurzbeschreibung (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. |
| Freie Schlagworte: | Biometrics, Morphing attack, Face recognition, Machine learning, Deep learning |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 16 Aug 2022 09:43 |
| Letzte Änderung: | 16 Nov 2022 09:09 |
| PPN: | 501691340 |
| Zugehörige Links: | |
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