This research introduces Adversarial Prompt Distillation (APD), a technique that significantly improves the robustness of Vision-Language Models against sophisticated attacks.

• Addresses critical security vulnerabilities in models like CLIP that are used in safety-critical applications
• Develops a multi-modal defense mechanism that outperforms existing single-modal approaches
• Achieves enhanced robustness while maintaining model performance
• Provides crucial protection for AI systems in high-stakes domains like autonomous driving and medical diagnosis

This research matters because it helps secure AI systems against malicious attacks in contexts where failures could have serious safety implications. By strengthening the resilience of vision-language models, the approach makes AI deployment safer in critical applications.

Adversarial Prompt Distillation for Vision-Language Models