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The main question this work aims at answering is: "can morphing attackdetection (MAD) solutions be successfully developed based on synthetic data?".Towards that, this work introduces the first synthetic-based MAD developmentdataset, namely the Synthetic Morphing Attack Detection Development dataset(SMDD). This dataset is utilized successfully to train three MAD backboneswhere it proved to lead to high MAD performance, even on completely unknownattack types. Additionally, an essential aspect of this work is the detailedlegal 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 availablefor research purposes.

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