Abstract
Recently, significant progress has been made in face presentation attack detection (PAD), which aims to secure face recognition systems against presentation attacks, owing to the availability of several face PAD datasets. However, all available datasets are based on privacy and legally-sensitive authentic biometric data with a limited number of subjects. To target these legal and technical challenges, this work presents the first synthetic-based face PAD dataset, named SynthASpoof, as a large-scale PAD development dataset. The bona fide samples in SynthASpoof are synthetically generated and the attack samples are collected by presenting such synthetic data to capture systems in a real attack scenario. The experimental results demonstrate the feasibility of using SynthASpoof for the development of face PAD. Moreover, we boost the performance of such a solution by incorporating the domain generalization tool MixStyle into the PAD solutions. Additionally, we showed the viability of using synthetic data as a supplement to enrich the diversity of limited authentic training data and consistently enhance PAD performances. The SynthASpoof dataset, containing 25,000 bona fide and 78,800 attack samples, the implementation, and the pre-trained weights are made publicly available.
Abstract (translated)
最近,在面部展示攻击检测(PAD)方面取得了重要进展,旨在确保面部识别系统免受展示攻击。由于有几个面部PAD数据集的可用性,这篇论文提出了第一个基于合成的面部PAD数据集,名为 SynthASpoof,作为大规模PAD开发数据集。在SynthASpoof中,真阳性样本是通过合成数据捕获系统的 real-attack 场景收集的。实验结果表明,使用 SynthASpoof 开发面部 PAD 是可行的。此外,我们还可以通过将领域泛化工具 MixStyle 添加到 PAD 解决方案中来提高该解决方案的性能。此外,我们还展示了使用合成数据作为补充以丰富有限 authentic 训练数据的多样性,并持续提高 PAD 性能的可能性。 SynthASpoof 数据集包含 25,000 个真阳性样本和 78,800 个攻击样本,实现和预训练权重已公开可用。
URL
https://arxiv.org/abs/2303.02660
