Awesome

Adversarial Robustness

This repository contains the unofficial implementation of the papers "Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples" (Gowal et al., 2020) and "Fixing Data Augmentation to Improve Adversarial Robustness" (Rebuffi et al., 2021) in PyTorch.

Requirements

The code has been implemented and tested with Python 3.8.5 and PyTorch 1.8.0. To install the required packages:

$ pip install -r requirements.txt

Usage

Training

Run train-wa.py for reproducing the results reported in the papers. For example, train a WideResNet-28-10 model via TRADES on CIFAR-10 with the additional pseudolabeled data provided by Carmon et al., 2019 or the synthetic data from Rebuffi et al., 2021 (without CutMix):

$ python train-wa.py --data-dir <data_dir> \
    --log-dir <log_dir> \
    --desc <name_of_the_experiment> \
    --data cifar10s \
    --batch-size 1024 \
    --model wrn-28-10-swish \
    --num-adv-epochs 400 \
    --lr 0.4 \
    --beta 6.0 \
    --unsup-fraction 0.7 \
    --aux-data-filename <path_to_additional_data>

Note: Note that with Gowal et al., 2020, expect about 0.5% lower robust accuracy than that reported in the paper since the original implementation uses a custom regenerated pseudolabeled dataset which is not publicly available (See Section 4.3.1 here).

Robustness Evaluation

The trained models can be evaluated by running eval-aa.py which uses AutoAttack for evaluating the robust accuracy. For example:

$ python eval-aa.py --data-dir <data_dir> \
    --log-dir <log_dir> \
    --desc <name_of_the_experiment>

For PGD evaluation:

$ python eval-adv.py --wb --data-dir <data_dir> \
    --log-dir <log_dir> \
    --desc <name_of_the_experiment>

Reference & Citing this work

If you use this code in your research, please cite the original works [Paper] [Code in JAX+Haiku] [Pretrained models]:

@article{gowal2020uncovering,
    title={Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples},
    author={Gowal, Sven and Qin, Chongli and Uesato, Jonathan and Mann, Timothy and Kohli, Pushmeet},
    journal={arXiv preprint arXiv:2010.03593},
    year={2020},
    url={https://arxiv.org/pdf/2010.03593}
}

and/or

@article{rebuffi2021fixing,
  title={Fixing Data Augmentation to Improve Adversarial Robustness},
  author={Rebuffi, Sylvestre-Alvise and Gowal, Sven and Calian, Dan A. and Stimberg, Florian and Wiles, Olivia and Mann, Timothy},
  journal={arXiv preprint arXiv:2103.01946},
  year={2021},
  url={https://arxiv.org/pdf/2103.01946}
}

and this repository:

@misc{rade2021pytorch,
    title = {{PyTorch} Implementation of Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples},
    author = {Rade, Rahul},
    year = {2021},
    url = {https://github.com/imrahulr/adversarial_robustness_pytorch}
}