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Adversarial machine learning is a field of study that focuses on the vulnerabilities of machine learning models in the presence of adversarial inputs. Specifically, it investigates how malicious actors might exploit weaknesses in machine learning algorithms to deceive them or cause them to misclassify data. This area combines insights from machine learning, statistics, and game theory to understand and defend against such attacks.