Robustness ensures a model's behavior remains predictable and consistent even under stress.
Attackers exploit the optimization process used to train models, finding "blind spots" in the decision boundary. Chapter 1 - Introduction to adversarial robustness Machine Learning Algorithms: Adversarial Robust...
As AI moves from research labs into safety-critical domains like autonomous driving , healthcare , and financial systems , vulnerabilities become physical risks. Attacks can cause self-driving cars to misidentify stop
Attacks can cause self-driving cars to misidentify stop signs or bypass security filters in large language models. Why Adversarial Robustness is Critical
Adversarial robustness in machine learning (ML) refers to a model's ability to maintain accurate performance even when faced with —inputs specifically designed by a malicious actor to trick the model into making incorrect predictions. While a standard model might achieve high accuracy on normal data, it can be remarkably brittle when confronted with these subtle, often imperceptible, perturbations. Why Adversarial Robustness is Critical