https://github.com/nvedant07/Fairness-Through-Robustness

Official code for FAccT'21 paper "Fairness Through Robustness: Investigating Robustness Disparity in Deep Learning" https://arxiv.org/abs/2006.12621
https://github.com/nvedant07/Fairness-Through-Robustness

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Official code for FAccT'21 paper "Fairness Through Robustness: Investigating Robustness Disparity in Deep Learning" https://arxiv.org/abs/2006.12621

• Host: GitHub
• URL: https://github.com/nvedant07/Fairness-Through-Robustness
• Owner: nvedant07
• Created: 2021-01-14T15:47:09.000Z (over 3 years ago)
• Default Branch: master
• Last Pushed: 2021-03-09T13:30:09.000Z (over 3 years ago)
• Last Synced: 2024-03-01T00:39:31.032Z (4 months ago)
• Language: Jupyter Notebook
• Size: 2.29 MB
• Stars: 10
• Watchers: 1
• Forks: 1
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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• awesome-stars - nvedant07/Fairness-Through-Robustness - Official code for FAccT'21 paper "Fairness Through Robustness: Investigating Robustness Disparity in Deep Learning" https://arxiv.org/abs/2006.12621 (Jupyter Notebook)

README

        
## Fairness Through Robustness: Investigating Robustness Disparity in Deep Learning

Official code to replicate results in [Fairness Through Robustness: Investigating Robustness Disparity in Deep Learning](https://arxiv.org/abs/2006.12621), presented at [FAccT 2021](https://facctconference.org/2021/acceptedpapers.html) in ~~Toronto, Canada~~ over Zoom.

Authors:

 * [Vedant Nanda*](http://nvedant07.github.io/)

 * [Samuel Dooley*](https://www.cs.umd.edu/~sdooley1/)

 * [Sahil Singla](https://scholar.google.co.in/citations?user=jjjbOI4AAAAJ&hl=en)

 * [Soheil Feizi](https://www.cs.umd.edu/~sfeizi/)

 * [John P. Dickerson](http://jpdickerson.com/)

Adversarial attacks in our code use [`foolbox`](https://github.com/bethgelab/foolbox). Code under `./smoothing_code` is taken from https://github.com/locuslab/smoothing and https://github.com/Hadisalman/smoothing-adversarial. We thank the authors of these repos for making their code public and easy to use.

### Requirements

 * python 3.7.x or higher

 * CUDA 10.1

python dependencies

 * To install all dependencies run `pip install -r requirements.txt`

 * NOTE: Our implementation uses foolbox v2.4.0. If not available on PyPi, we recommend building it from source. Follow instructions on the [Foolbox repo](https://github.com/bethgelab/foolbox) to build from source.

### Datasets

We use CIFAR-10, CIFAR-100, UTKFace and Adience for our analysis. See `./data` for more information on how to download the datasets.

### Training Models

We've released pre-trained weights used in our analysis (coming soon!). You can either use these weights or train models on your own.

#### Working with pre-trained weights

> Coming soon!

#### Training models

See `./code/experiment-main.py`.

### Evaluating Robustness Bias

This step requires running adversarial attacks (in our paper we evaluate [DeepFool](https://openaccess.thecvf.com/content_cvpr_2016/html/Moosavi-Dezfooli_DeepFool_A_Simple_CVPR_2016_paper.html) and [Carlini&Wagner](https://ieeexplore.ieee.org/abstract/document/7958570/)). 

#### Running Adversarial Attacks

See `./code/experiment-adversarial.py`

#### Running Randomized Smoothing 

```

cd smoothing_code

python certify.py cifar10   0.125  --batch 100 --alpha 0.001 --N0 100 --N 100000 --skip 5 --gpu 

```

#### Analysis

See `./code/certificate_analysis/` for analysis of randomized smoothing and `./code/experiment_adversarial_only_plot.ipynb` for analysis of adversarial attacks.

### Citation

If you found our work useful, please cite it. 

```

@inproceedings{nanda2021fairness,

    author = {Nanda, Vedant and Dooley, Samuel and Singla, Sahil and Feizi, Soheil and Dickerson, John P.},

    title = {Fairness Through Robustness: Investigating Robustness Disparity in Deep Learning},

    year = {2021},

    isbn = {9781450383097},

    publisher = {Association for Computing Machinery},

    address = {New York, NY, USA},

    url = {https://doi.org/10.1145/3442188.3445910},

    doi = {10.1145/3442188.3445910},

    booktitle = {Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency},

    pages = {466–477},

    numpages = {12},

    location = {Virtual Event, Canada},

    series = {FAccT '21}

}

```

### Feedback

We'd love to hear from you! Write to us at `[email protected]` or `[email protected]`.