What is adversarial robustness in deployed ML systems?

Adversarial robustness ensures ML models resist intentionally crafted malicious inputs.

In production ML, adversarial attacks manipulate inputs to cause incorrect predictions. Robust systems use techniques like adversarial training, input sanitization, gradient masking, and anomaly detection. This is critical in security-sensitive domains like fraud detection and biometric authentication.