Abstract
Recent advances in biometric systems have significantly improved the detection and prevention of fraudulent activities. However, as detection methods improve, attack techniques become increasingly sophisticated. Attacks on face recognition systems can be broadly divided into physical and digital approaches. Traditionally, deep learning models have been the primary defence against such attacks. While these models perform exceptionally well in scenarios for which they have been trained, they often struggle to adapt to different types of attacks or varying environmental conditions. These subsystems require substantial amounts of training data to achieve reliable performance, yet biometric data collection faces significant challenges, including privacy concerns and the logistical difficulties of capturing diverse attack scenarios under controlled conditions. This work investigates the application of Vision Language Models (VLM) and proposes an in-context learning framework for detecting physical presentation attacks and digital morphing attacks in biometric systems. Focusing on open-source models, the first systematic framework for the quantitative evaluation of VLMs in security-critical scenarios through in-context learning techniques is established. The experimental evaluation conducted on freely available databases demonstrates that the proposed subsystem achieves competitive performance for physical and digital attack detection, outperforming some of the traditional CNNs without resource-intensive training. The experimental results validate the proposed framework as a promising tool for improving generalisation in attack detection.
Abstract (translated)
最近在生物识别系统方面的进展显著提高了检测和预防欺诈行为的能力。然而，随着检测方法的改进，攻击技术也变得越来越复杂。针对面部识别系统的攻击可以大致分为物理攻击和数字攻击两大类。传统上，深度学习模型一直是防御此类攻击的主要手段。尽管这些模型在其训练场景中表现出色，但它们往往难以适应不同类型或环境条件变化的攻击。为了实现可靠的性能，这类子系统需要大量的训练数据，而生物识别数据收集面临着包括隐私担忧及在控制条件下捕获各种攻击情景的实际困难等重大挑战。 本文研究了视觉语言模型（VLM）的应用，并提出了一种通过上下文学习框架检测生物识别系统中物理呈现攻击和数字变形攻击的方法。专注于开源模型，建立了首个基于上下文学习技术对安全关键场景中VLM进行定量评估的系统性框架。在公开数据库上进行的实验评估表明，所提出的子系统对于物理和数字攻击检测均实现了竞争性的性能，并且无需资源密集型训练即可超越某些传统的CNN（卷积神经网络）。这些实验结果验证了所提框架作为改进攻击检测泛化能力的有前景工具的有效性。
URL
https://arxiv.org/abs/2507.15285
