  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.
