Explore the concept of adversarial training in machine learning through this 34-minute lecture from the University of Central Florida. Delve into the motivation behind revisiting adversarial training techniques, examining the technical contributions and background of the topic. Analyze the problems associated with Projected Gradient Descent (PGD) and compare various approaches. Review experimental results, including those on ImageNet, and consider the strengths and arguments presented in the conclusion. Gain valuable insights into the latest developments in adversarial machine learning and their implications for model robustness and security.