mhilmiasyrofi / mma_training

Code for the paper "MMA Training: Direct Input Space Margin Maximization through Adversarial Training"

MMA Training (Max-Margin Adversarial Training)

This repo contains code for MMA Training: Direct Input Space Margin Maximization through Adversarial Training (ICLR 2020) by Gavin Weiguang Ding, Yash Sharma, Kry Yik Chau Lui, and Ruitong Huang.

Dependencies

• pytorch (1.0.0)
• torchvision (0.2.1)
• advertorch (0.2.2)

The code is tested with library versions specified above. It might also work with later versions.

Overview of files

anpgd.py implements the AN-PGD attack used for MMA training. config.py contains all the default training hyperparameters. utils.py provides utility functions for MMA training. trainer.py implements the MMA training algorithm. train.py runs the MMA training process. evaluate_on_pgd_attacks.py run_mnist_training.sh and run_cifar10_training.sh contain commands for reproducing MMA models in the paper. trained_models contains pretrained MMA models. attack_mnist_models.sh and attack_cifar10_models.sh contain command for evaluating MMA models with repeated whitebox PGD attacks.

Examples

To train a MMA model on CIFAR10 with d_max=32/255 under Linf attacks, run

python train.py --dataset cifar10 --norm Linf --hinge_maxeps 0.1255 --seed 0 --savepath ./trained_models/cifar10-Linf-MMA-32-sd0

After training, to evaluate this model under Linf attacks with epsilon=8/255, run

python evaluate_on_pgd_attacks.py --dataset cifar10 --norm Linf --eps 0.0314 --seed 0 --model ./trained_models/cifar10-Linf-MMA-32-sd0/model_best.pt

See run_mnist_training.sh and run_cifar10_training.sh for the complete list.

Reference

bibtex entry:

@inproceedings{
Ding2020MMA,
title={{MMA} Training: Direct Input Space Margin Maximization through Adversarial Training},
author={Ding, Gavin Weiguang and Sharma, Yash and Lui, Kry Yik Chau and Huang, Ruitong},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=HkeryxBtPB}
}