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.

本研究介绍了首个基于合成数据的变形攻击检测开发数据集 (SMDD) ，证明该数据集成功地用于训练三种变形攻击检测骨干网络，从而实现了高性能的变形攻击检测，即使是完全未知的攻击类型。通过详细的法律分析，我们表明在使用和分享真实生物特征数据的挑战方面，我们提议的 SMDD 数据集至关重要。该数据集包含 30,000 个攻击样本和 50,000 个真实样本，可供研究目的公开使用。