README.md

Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers:

This repository is for the paper

• "Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers" by Guang-He Lee, Yang Yuan, Shiyu Chang, and Tommi S. Jaakkola in NeurIPS 2019.
• The old title for this paper is "A Stratified Approach to Robustness for Randomly Smoothed Classifiers"

Outline

• Please see each experiment in the corresponding directory (and the README therein).
• The MNIST experiment has been released.
• The ImageNet experiment has been released. (Not carefully checked. Please let me know if you find any problem.)
• The pre-computed ρ^-1_r(0.5) and trained ResNet50 models have been released for the ImageNet experiment.
• If you want to compute your own ρ^-1_r(0.5), please see the examples in the MNIST or ImageNet folder.
• Please let me know (guanghe@mit.edu) if you need the codes for the decision tree experiment.

Citation:

If you find this repo useful for your research, please cite the paper

@inproceedings{lee2019tight,
  title={Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers},
  author={Guang-He Lee and Yang Yuan and Shiyu Chang and Tommi S. Jaakkola},
  booktitle={Advances in Neural Information Processing Systems},
  year={2019}
}