Adversarial machine learning is a research domain focused on understanding and mitigating the vulnerabilities of machine learning models to deliberately crafted inputs designed to cause misclassification or other undesirable behavior. It encompasses the study of generating such 'adversarial examples', analyzing their impact, and developing robust models and defensive techniques to resist them, highlighting critical challenges in the security, reliability, and trustworthiness of AI systems.