论文标题
隐私友好的合成数据，用于开发面部变形攻击探测器
Privacy-friendly Synthetic Data for the Development of Face Morphing Attack Detectors
论文作者
论文摘要
这项工作旨在回答的主要问题是：“可以根据合成数据成功开发变形攻击检测（MAD）解决方案吗？”。在此方面，这项工作介绍了第一个基于合成的MAD开发数据集，即合成变形攻击检测开发数据集（SMDD）。该数据集被成功地用于训练三个疯狂的骨干，即使在完全未知的攻击类型上，它也会导致高表现。此外，这项工作的一个重要方面是对使用和共享实际生物识别数据的挑战的详细法律分析，使我们提出的SMDD数据集非常重要。 SMDD数据集由30,000次攻击和50,000个真正的善意样本组成，可公开用于研究目的。
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.
