This research introduces a new approach to improve facial authentication security by making spoofing detection systems work more reliably across different environments and devices.

Key Innovations:

• Combines content-aware prompts with composite prompt engineering to better recognize subtle spoofing clues
• Overcomes limitations of existing CLIP-based methods by creating more semantically meaningful associations
• Achieves superior domain generalization performance compared to previous state-of-the-art approaches
• Provides robust protection against facial spoofing attacks without requiring training on target domains

Security Impact: This advancement significantly enhances biometric authentication systems by reducing false acceptances across different usage environments, creating more trustworthy facial recognition for secure access control applications.

Domain Generalization for Face Anti-spoofing via Content-aware Composite Prompt Engineering