Consistency Regularization for Adversarial Robustness

Official PyTorch implementation of Consistency Regularization for Adversarial Robustness (AAAI 2022) by Jihoon Tack, Sihyun Yu, Jongheon Jeong, Minseon Kim, Sung Ju Hwang, and Jinwoo Shin.

1. Dependencies

conda create -n con-adv python=3
conda activate con-adv

conda install pytorch torchvision cudatoolkit=11.0 -c pytorch 

pip install git+https://github.com/fra31/auto-attack
pip install advertorch tensorboardX

2. Training

2.1. Training option and description

The option for the training method is as follows:

• <DATASET>: {cifar10,cifar100,tinyimagenet}
• <AUGMENT>: {base,autoaug,autoaug_sche}
• <ADV_TRAIN OPTION>: {adv_train,adv_trades,adv_mart}

Current code are assuming l_infinity constraint adversarial training and PreAct-ResNet-18 as a base model.
To change the option, simply modify the following configurations:

• WideResNet-34-10: --model wrn3410
• l_2 constraint: --distance L2

2.2. Training code

Standard cross-entropy training

# Standard cross-entropy
python train.py --mode ce --augment_type base --dataset <DATASET>

Adversarial training

# Adversarial training
python train.py --mode <ADV_TRAIN OPTION> --augment_type <AUGMENT> --dataset <DATASET>

# Example: Standard AT under CIFAR-10
python train.py --mode adv_train --augment_type base --dataset cifar10

Consistency regularization

# Consistency regularization
python train.py --consistency --mode <ADV_TRAIN OPTION> --augment_type <AUGMENT> --dataset <DATASET>

# Example: Consistency regularization based on standard AT under CIFAR-10
python train.py --consistency --mode adv_train --augment_type autoaug_sche --dataset cifar10 

3. Evaluation

3.1. Evaluation option and description

The description for treat model is as follows:

• <DISTANCE>: {Linf,L2,L1}, the norm constraint type
• <EPSILON>: the epsilon ball size
• <ALPHA>: the step size of PGD optimization
• <NUM_ITER>: iteration number of PGD optimization

3.2. Evaluation code

Evaluate clean accuracy

python eval.py --mode test_clean_acc --dataset <DATASET> --load_path <MODEL_PATH>

Evaluate clean & robust accuracy against PGD

python eval.py --mode test_adv_acc --distance <DISTANCE> --epsilon <EPSILON> --alpha <ALPHA> --n_iters <NUM_ITER> --dataset <DATASET> --load_path <MODEL_PATH>

Evaluate clean & robust accuracy against AutoAttack

python eval.py --mode test_auto_attack --epsilon <EPSILON> --distance <DISTANCE> --dataset <DATASET> --load_path <MODEL_PATH>

Evaluate mean corruption error (mCE)

python eval.py --mode test_mce --dataset <DATASET> --load_path <MODEL_PATH>

Citation

@inproceedings{tack2022consistency,
  title={Consistency Regularization for Adversarial Robustness},
  author={Jihoon Tack and Sihyun Yu and Jongheon Jeong and Minseon Kim and Sung Ju Hwang and Jinwoo Shin},
  booktitle={AAAI Conference on Artificial Intelligence},
  year={2022}
}