arXiv:2607.01303v1 Announce Type: cross Abstract: Presentation Attack Detection (PAD) serves as a crucial safeguard for face recognition systems against presentation attacks such as printed photos, replayed videos, and 3D masks. Despite significant progress, existing PAD models still struggle to generalize across unseen domains due to variations in sensors, lighting, and attack materials. Recent Vision-Language Models (VLMs) have shown strong generalization ability, yet their applications in PAD remain limited because learned prompts, typically optimized under class-label supervision, fail to
The development of sophisticated AI models and deepfake technologies necessitates equally advanced countermeasures to maintain security and trust in digital identification systems.
Improving Presentation Attack Detection (PAD) is critical for securing face recognition systems, which are increasingly integral to finance, access control, and national security.
The application of concept-informed prompts within Vision-Language Models for PAD could significantly enhance the robustness and generalization ability of fraud detection in biometric systems.
- · Biometric security companies
- · Financial institutions
- · Facial recognition system developers
- · Attackers using presentation attacks
- · Fraudsters
Enhanced security for systems relying on facial recognition reduces fraud and unauthorized access.
Increased public and institutional trust in biometric authentication accelerates its adoption across various sectors.
The development of more sophisticated AI-driven defense mechanisms could spark an arms race between AI security and attack vectors.
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Read at arXiv cs.AI