Attacking machine learning with adversarial examples

Adversarial examples are specially crafted inputs designed to fool machine learning models into making incorrect predictions, functioning like optical illusions for AI systems. The article explores how these attacks work across different mediums and highlights the challenges in defending ML systems against such vulnerabilities.