Adversarial Learning is a training approach that exposes models to deliberately challenging, manipulative, or attack-like inputs. Its purpose is to improve robustness, security, and reliability against adversarial behavior or distributional stress.
A side-by-side comparison of Data Poisoning and Adversarial Learning. Understand how the terms differ, when each applies, and what the distinction means for AI governance, system design, or assurance evidence.