In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems

• URL: http://arxiv.org/abs/2507.15285v1
• Date: Mon, 21 Jul 2025 06:35:46 GMT
• Title: In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems
• Authors: Lazaro Janier Gonzalez-Soler, Maciej Salwowski, Christoph Busch,
• Abstract summary: Attacks on face recognition systems can be divided into physical and digital approaches.<n>Deep learning models have been the primary defence against such attacks.<n>This work proposes an in-context learning framework for detecting physical presentation attacks and digital morphing attacks.
• Score: 4.3415446824106745
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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