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Improving Ensemble Robustness Evaluation with Model-Reweighing Attack (NeurIPS 2022)

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// repository documentation

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# MORA

This repository contains code
for reproducing the results in our NeurIPS 2022 paper
["MORA: Improving Ensemble Robustness Evaluation with Model-Reweighing Attack"](https://openreview.net/pdf?id=d_m7OKOmPiM).

Please feel free to cite our paper with the following bibtex entry:
```bibtex
@inproceedings{mora,
 author = {Yu, Yunrui and Gao, Xitong and Xu, Cheng-Zhong},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh},
 pages = {26955--26965},
 publisher = {Curran Associates, Inc.},
 title = {{MORA}: Improving Ensemble Robustness Evaluation with Model Reweighing Attack},
 url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/ac895e51849bfc99ae25e054fd4c2eda-Paper-Conference.pdf},
 volume = {35},
 year = {2022}
}
```

## Dependencies

Create the conda environment called `mora`
containing all the dependencies by running:
```shell
conda env create -f environment.yml
```
We used PyTorch 1.4.0 for all the experiments,
and the code were tested on an NVIDIA TITAN Xp GPU.

## Pretrained models

The pretrained models
for the ensemble defense strategies (ADP, DVERGE, GAL)
can be accessed
via [this link](https://drive.google.com/drive/folders/1i96Bk_bCWXhb7afSNp1t3woNjO1kAMDH?usp=sharing).
The pre-trained models are located
in the folder named `checkpoints`.
Download and place the checkpoints
into a `checkpoints/` folder under this repo
before running evaluation scripts.

## Usage

Examples of evaluation scripts can be found in `scripts/evaluation.sh`.

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