Explore a comprehensive lecture on adaptive attacks to adversarial example defenses, focusing on various techniques and strategies used in machine learning security. Delve into key concepts such as expectation over transformation, K winners take all, chaos partitioning, and local gradient estimation. Examine the intricacies of noise manipulation, mixup interference, and methods for attacking adversarial examples. Gain valuable insights into the latest developments in this critical area of artificial intelligence and cybersecurity.