Adversarial Machine Learning

This lecture explores the concept of adversarial machine learning, focusing on how small perturbations can cause misclassifications with high confidence in neural networks. It delves into the security implications, risks, and vulnerabilities associated with adversarial examples. The instructor discusses the generation of adversarial examples, different attack methods, and the challenges in optimizing classification losses. Additionally, the lecture covers white-box and black-box attacks, transfer attacks, and the importance of physically realizable attacks. The instructor also explains adversarial training as a method to train robust models and the trade-off between accuracy and robustness.