Certified Robustness to Adversarial Examples with Differential Privacy

Explore a conference talk on certified robustness to adversarial examples using differential privacy. Delve into the innovative PixelDP defense, which scales to large networks and datasets while providing guarantees against norm-bounded attacks. Learn about the connection between adversarial robustness and differential privacy, and understand how this approach offers a rigorous, generic, and flexible foundation for defending machine learning models, particularly deep neural networks. Examine the application of this defense to large-scale networks like Google's Inception for ImageNet, and gain insights into key questions, challenges, and results related to this cutting-edge security research.

Intro
Adversarial Examples
ImageNet Network
Key Questions
Core Problem
Differential Privacy
Summary
Challenges
Scale
Autoencoder
Postprocessing
Guarantees
Results
Robustness threshold
Summarize
Questions