Explaining and Harnessing Adversarial Examples in Machine Learning - Spring 2021
Explore the concept of adversarial examples in machine learning through this 27-minute lecture from the University of Central Florida. Delve into the history, motivation, and application of adversarial examples for both linear and non-linear models. Examine the Fast Gradient Sign Method (FGSM) and its experimental results on neural networks with different activation functions. Learn about adversarial training techniques for logistic regression and deep neural networks, comparing their effectiveness to L1 regularization. Investigate the generalization of adversarial examples across different models and consider alternative hypotheses. Analyze the strengths and weaknesses of adversarial training approaches, gaining a comprehensive understanding of this important topic in machine learning security and robustness.