Boosting Adversarial Training with Mitigating Hard Sample Interference Code for the paper Boosting Adversarial Training with Mitigating Hard Sample Interference.

OS: Ubuntu 20.04.3
GPU: NVIDIA A100
Cuda: 11.1, Cudnn: v8.2
Python: 3.9.5
PyTorch: 1.8.0
Torchvision: 0.9.0
Acknowledgement
The codes are modifed based on the PyTorch implementation of Rebuffi et al., 2021.

Install or download AutoAttack:
pip install git+https://github.com/fra31/auto-attack

|Dataset Extra | Size | Link |
|CIFAR-10 |DDPM 1M | npz |
|CIFAR-100 |DDPM 1M | npz |
|SVHN |DDPM 1M | npz |

$python train-wa.py --data-dir 'cifar-data' \
    --log-dir 'trained_models' \
    --desc 'WRN28-10Swish_cifar10s_lr0p2_TRADES5_epoch400_bs512_fraction0p7_ls0p1' \
    --data cifar10s \
    --batch-size 512 \
    --model wrn-28-10-swish \
    --num-adv-epochs 400 \
    --lr 0.2 \
    --beta 5.0 \
    --unsup-fraction 0.7 \
    --aux-data-filename 'cifar10_ddpm.npz' \
    --ls 0.1

Here --ls 0.1 is inherent from the the code implementation of Rebuffi et al., 2021.

python train-wa.py --data-dir 'cifar-data' \
    --log-dir 'trained_models' \
    --desc 'WRN28-10Swish_cifar10s_lr0p2_TRADES4_epoch400_bs512_fraction0p7_LSE' \
    --data cifar10s \
    --batch-size 512 \
    --model wrn-28-10-swish \
    --num-adv-epochs 10 \
    --lr 0.005 \
    --beta 4.0 \
    --unsup-fraction 0.7 \
    --aux-data-filename 'cifar10_ddpm.npz' \
    --LSE --ls 0

Here we only need to activate the flag --LSE and set --ls 0.

Below are pretrained checkpoints of WRN-28-10 (Swish) and WRN-70-16 (Swish) with --beta=3.0:

Downloading checkpoint to trained_models/mymodel/weights-best.pt Downloading argtxt to trained_models/mymodel/args.txt

For evaluation under AutoAttack, run the command (taking our method as an example):

python eval-aa.py --data-dir 'cifar-data'
--log-dir 'trained_models'
--desc mymodel

python eval-adv.py --data-dir 'cifar-data'
--log-dir 'trained_models'
--desc mymodel

python eval-muti-adv.py --data-dir 'cifar-data'
--log-dir 'trained_models'
--desc mymodel