Machine Learning Security

This lecture covers the basics of machine learning security, including stealing models, altering model outputs, issues with federated learning, and pitfalls in machine learning taxonomy. It also delves into adversarial conditions, model stealing, machine learning as a service, and privacy challenges in machine learning. The instructor discusses the implications of attacks against neural networks, defenses against model stealing, and the challenges of preventing adversarial examples. The lecture concludes with insights on biases, fallacies, and the importance of addressing biases in machine learning models.