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
Regulations like the EU AI Act now mandate adversarial robustness for high-risk AI systems. Common Adversarial Attacks Machine Learning Algorithms: Adversarial Robust...
Attackers exploit the optimization process used to train models, finding "blind spots" in the decision boundary. Chapter 1 - Introduction to adversarial robustness Adversarial robustness in machine learning (ML) refers to
Attacks can cause self-driving cars to misidentify stop signs or bypass security filters in large language models. and financial systems
As AI moves from research labs into safety-critical domains like autonomous driving , healthcare , and financial systems , vulnerabilities become physical risks.
Robustness ensures a model's behavior remains predictable and consistent even under stress.