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

Naser Damer, César Augusto Fontanillo López, Meiling Fang, Noémie Spiller, Minh Vu Pham, Fadi Boutros; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2022, pp. 1606-1617

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.

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[bibtex]
@InProceedings{Damer_2022_CVPR, author = {Damer, Naser and L\'opez, C\'esar Augusto Fontanillo and Fang, Meiling and Spiller, No\'emie and Pham, Minh Vu and Boutros, Fadi}, title = {Privacy-Friendly Synthetic Data for the Development of Face Morphing Attack Detectors}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2022}, pages = {1606-1617} }