Official repo for the paper "Make Some Noise: Reliable and Efficient Single-Step Adversarial Training" (https://arxiv.org/abs/2202.01181)
You may create a conda environment from requirements.txt
conda create --name <env> --file requirements.txt
In general the code should be compatible with pytorch >= 1.7.1.
You may perform N-FGSM Adversarial Training with CIFAR10, CIFAR100 and SVHN datasets and PreActResNet18 and WideResNet28-10. For instance, to train on CIFAR10 with preactresnet18:
python train.py --epsilon 8 --alpha 8 --unif 2 --dataset CIFAR10 --architecture preactresnet18 --out-dir path/to/results --root-model-dir /path/to/trained/models/
The configurations for all datasets, models and epsilon radii can be found in experiments.sh
If you use this code, please consider citing:
@inproceedings{
jorge2022make,
title={Make Some Noise: Reliable and Efficient Single-Step Adversarial Training},
author={Pau de Jorge and Adel Bibi and Riccardo Volpi and Amartya Sanyal and Philip Torr and Gr{\'e}gory Rogez and Puneet K. Dokania},
booktitle={Thirty-Sixth Conference on Neural Information Processing Systems},
year={2022},
url={https://openreview.net/forum?id=NENo__bExYu}
}