Adversarial Learning

A broad field of machine learning concerned with how AI systems behave when faced with inputs designed to deceive or manipulate them, and how to make those systems more robust. It encompasses both the offensive side, crafting adversarial examples and adversarial attacks, and the defensive side, covering adversarial training, input validation, and robustness testing. The term also underpins generative techniques like Generative Adversarial Networks, where two models are pitted against each other to improve output quality.