This research introduces a novel approach to detecting adversarial attacks across multiple vision systems without requiring attack-specific knowledge.

Key Innovations:

• Leverages pre-trained encoders to extract universal representations from input samples
• Achieves generalization across different attack types without specialized feature engineering
• Outperforms traditional detection methods that rely on handcrafted features
• Provides a more scalable and adaptable security solution for real-world AI systems

Security Implications: This framework significantly enhances AI system security by creating a unified defense mechanism against both known and novel adversarial attacks, protecting critical vision-based applications from malicious exploitation.

Unleashing the Power of Pre-trained Encoders for Universal Adversarial Attack Detection