Abstract
Although face recognition systems have undergone an impressive evolution in the last decade, these technologies are vulnerable to attack presentations (AP). These attacks are mostly easy to create and, by executing them against the system's capture device, the malicious actor can impersonate an authorised subject and thus gain access to the latter's information (e.g., financial transactions). To protect facial recognition schemes against presentation attacks, state-of-the-art deep learning presentation attack detection (PAD) approaches require a large amount of data to produce reliable detection performances and even then, they decrease their performance for unknown presentation attack instruments (PAI) or database (information not seen during training), i.e. they lack generalisability. To mitigate the above problems, this paper focuses on zero-shot PAD. To do so, we first assess the effectiveness and generalisability of foundation models in established and challenging experimental scenarios and then propose a simple but effective framework for zero-shot PAD. Experimental results show that these models are able to achieve performance in difficult scenarios with minimal effort of the more advanced PAD mechanisms, whose weights were optimised mainly with training sets that included APs and bona fide presentations. The top-performing foundation model outperforms by a margin the best from the state of the art observed with the leaving-one-out protocol on the SiW-Mv2 database, which contains challenging unknown 2D and 3D attacks
Abstract (translated)
尽管面部识别系统在过去的十年里经历了令人印象深刻的发展，但这些技术仍然容易受到攻击演示（AP）的影响。这些攻击通常是易于创建的，并且通过执行针对系统的捕捉设备，恶意行为者可以冒充授权用户并因此获得后者的个人信息（例如财务交易）。为了保护面部识别方案免受展示攻击，最先进的深度学习呈现攻击检测（PAD）方法需要大量的数据才能产生可靠的检测性能；即便如此，在面对未知呈现攻击工具（PAI）或未在训练期间见过的数据库信息时，它们的表现会下降，即缺乏泛化能力。为了解决上述问题，本文专注于零样本PAD。为此，我们首先评估了基础模型在已建立和具有挑战性的实验场景中的有效性和泛化性，并提出了一种简单的但有效的零样本PAD框架。实验结果表明，这些模型能够在困难的场景中以最少的努力实现高级PAD机制所达到的性能，而后者的主要训练集包括APs和真实的面部呈现数据。顶级表现的基础模型在SiW-Mv2数据库（包含具有挑战性的未知2D和3D攻击）上采用leave-one-out协议时的表现优于当前最佳方法。
URL
https://arxiv.org/abs/2507.16393
