Adversarial computing involves studying the properties and behaviors of systems under hostile conditions, particularly in cybersecurity and machine learning contexts. It deals with how systems can be fooled by adversarial examples — inputs designed to mislead AI models — and emphasizes the creation of more robust algorithms that can withstand such attacks. Researchers analyze different types of adversarial attacks, develop strategies for defense, and assess the impact of adversarial environments on machine learning models and computational processes.