hubin111 / MHSI

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

MHSI

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

Environment settings and libraries we used in our experiments

This project is tested under the following environment settings:

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.

Requirements

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

Download 1M DDPM generated data from the official implementation of Rebuffi et al., 2021:

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

Training Commands

To run the KL-based baselines (with 1M DDPM generated data), an example is:

$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.

To run our methods (with 1M DDPM generated data), an example is:

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.

Pretrained checkpoints

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

Evaluation Commands

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