Awesome
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<sup>-1</sup><sub>r</sub>(0.5) and trained ResNet50 models have been released for the ImageNet experiment.
- If you want to compute your own ρ<sup>-1</sup><sub>r</sub>(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}
}