guanghelee / Randomized_Smoothing

"Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers" (NeurIPS 2019, previously called "A Stratified Approach to Robustness for Randomly Smoothed Classifiers")

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 ([e-mail]) 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}
}