Optimal Transport as a Defense Against Adversarial Attacks

This repository contains the reference source code for the paper Optimal Transport as a Defense Against Adversarial Attacks (ICPR'20). For more information, please check out our paper, our poster or our presentation.

We propose a robust adversarial training method based on the latest theory of optimal transport and a new metric for robustness.

Requirements

• python==3.6+
• pytorch==1.6.0
• advertorch==0.2.0

You can run conda env create -f environment.yml to install required packages.

Train a model with SAT

To train a model with SAT on CIFAR10 use:

python train.py \
    --dataset 'cifar10' \
    -n 60 \
    --epoch_adv 0 \
    --gpu 0 \
    --arch 'resnet' \
    --sgd \
    --sink \
    --sink_eps 1 \
    -f "resnet_sink1.ckpt" \
    -c "_resnet_sink1"

You can use --dataset 'cifar100' to train on CIFAR100 and --arch 'wide' to train with a WideResnet.

Evaluate a model against adversarial attacks

To evaluate a model against an adversarial attack use:

python test_adv.py --arch 'resnet' --dataset 'cifar10' --eps 8 -a PGD -f resnet_sink1.ckpt -l 8 --gpu 0

You can choose the norm of the attack with -l 2 for an $L_2$ norm and -l 8 for an $L_\infty$ attack.

Compute AUAC

The AUAC is our proposed metric for robustness.

To compute AUAC you can use the provided function compute_auac in utils/utils.py:

from utils.utils import compute_auac

eps_list = [0,1,2,3,4,5]

acc_list = [96, 64.95, 34.52, 16.73, 7.81, 3.73]  
        
print(f"AUAC@5: {compute_auac(eps_list, acc_list)} %")

License

This repository is released under the CeCILL license, a free software license adapted to both international and French legal matters that is fully compatible with the FSF's GNU/GPL license.

Citation

If you find this repository useful for your own work, please cite our paper:

• Optimal Transport as a Defense Against Adversarial Attacks Quentin Bouniot, Romaric Audigier, Angélique Loesch. In ICPR 2020.

@InProceedings{Bouniot_2021_ICPR,
author = {Bouniot, Quentin and Audigier, Romaric and Loesch, Ang\'{e}lique},
title = {Optimal Transport As a Defense against Adversarial Attacks},
booktitle = {The IEEE 25th International Conference on Pattern Recognition (ICPR'20)},
month = {January},
year = {2021}
}