Adversarial learning is a subfield of machine learning where the goal is to train models to be robust against adversarial examples, which are carefully crafted inputs designed to fool the model into making incorrect predictions. This area of research focuses on developing algorithms and techniques that can defend against such attacks, as well as understanding the vulnerabilities of machine learning models and improving their overall security. Adversarial learning has applications in various domains, such as computer vision, natural language processing, and cybersecurity.