Adversarial systems refer to setups where two or more parties compete against each other, often with conflicting goals. These systems are commonly found in fields like artificial intelligence, game theory, and cybersecurity. In such environments, each participant tries to outsmart the others, leading to dynamic interactions and strategies. In the context of machine learning, adversarial systems can involve training models to recognize and defend against attacks that aim to mislead them. For example, an adversarial attack might involve subtly altering input data to trick a model into making incorrect predictions, highlighting the importance of robustness in AI systems.