Machine Learning Algorithms: Adversarial Robust... Apr 2026

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

As AI moves from research labs into safety-critical domains like autonomous driving , healthcare , and financial systems , vulnerabilities become physical risks. 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