Detection of T-shirt Presentation Attacks in Face Recognition Systems

2026-04-21

AI Summary

This study addresses the security threat posed by T-shirt-based presentation attacks, which can bypass existing face liveness detection systems and compromise biometric authentication. To tackle this vulnerability, the authors introduce TFPA, the first systematically constructed dataset comprising 1,608 T-shirt attack samples and 152 bona fide presentations. They propose a novel detection method that leverages spatial consistency between facial and full-body regions by modeling their geometric relationship using state-of-the-art face and human detection models. Experimental results demonstrate that the proposed approach effectively identifies T-shirt presentation attacks and significantly enhances generalization to unseen attack types, thereby improving the robustness and security of face-based biometric systems.

Technology Category

• Computer Vision: Biometrics, Face, Gesture & Pose
• Humans and AI: Interaction Techniques and Devices
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Face recognition systems are often used for biometric authentication. Nevertheless, it is known that without any protective measures, face recognition systems are vulnerable to presentation attacks. To tackle this security problem, methods for detecting presentation attacks have been developed and shown good detection performance on several benchmark datasets. However, generalising presentation attack detection methods to new and novel types of attacks is an ongoing challenge. In this work, we employ 1,608 T-shirt attacks of the T-shirt Face Presentation Attack (TFPA) database using 100 unique presentation attack instruments together with 152 bona fide presentations. In a comprehensive evaluation, we show that this type of attack can compromise the security of face recognition systems. Furthermore, we propose a detection method based on spatial consistency checks in order to detect said T-shirt attacks. Precisely, state-of-the-art face and person detectors are combined to analyse the spatial positions of detected faces and persons based on which T-shirt attacks can be reliably detected.

Problem

• presentation attack
• face recognition
• T-shirt attack
• biometric authentication
• attack generalisation

Innovation

• presentation attack detection
• T-shirt attack
• spatial consistency
• face recognition security
• multimodal detection