A New Perspective on Adversarial Perturbations
Explore a new perspective on adversarial perturbations in deep learning through this 49-minute lecture by Aleksander Madry from MIT. Delve into the key problem of adversarial perturbations and examine machine learning through the lens of adversarial robustness. Compare human and ML perspectives, analyze a simple experiment, and understand the robust features model. Investigate the differences between human and ML model priors, and discover new capabilities such as robustification and transferability. Learn about the role of robust training, gain a new take on randomized smoothing, and explore the relationship between robustness and data efficiency. Examine a simple theoretical setting using max likelihood Gaussian classification, and understand how robustness aligns with perception and computer vision applications. Conclude by recognizing that adversarial examples arise from non-robust features in the data.