Adversarial Machine Learning

Adversarial machine learning (AML) explores how to attack and defend against malicious manipulations of machine learning models. Current research focuses on developing novel attack strategies, particularly for real-time applications and diverse model types (e.g., image classifiers, language models, reinforcement learning agents), and designing robust defenses, including methods like adversarial training and purification models. The significance of AML research lies in its crucial role in securing increasingly prevalent machine learning systems across various sectors, from autonomous vehicles and 5G networks to malware detection and medical imaging, mitigating the risks posed by these vulnerabilities.