AWM

This repository contains our implementation of One-shot Neural Backdoor Erasing via Adversarial Weight Masking (accepted by NeurIPS 2022). [openreview]

Prerequisites

• PyTorch
• CUDA

Usage Examples:

python main.py --dataset cifar10 --batch-size 128 --sample 500 --attack trojan-sq
python main.py --dataset gtsrb --batch-size 32 --sample 43 --trigger-norm 100 --attack a2a --arch small_vgg
python main.py --dataset cifar10 --batch-size 32 --sample 10 --trigger-norm 100 --attack badnets --alpha 0.99

We have released some checkpoints: https://drive.google.com/drive/folders/150Egil8yrot3ppdROf39fXDvh8jYYDvl?usp=sharing Please add the datasets under /data folder to use.

Citation

Please check our paper for technical details and full results. If you find our paper useful, please cite:

@inproceedings{
chai2022oneshot,
title={One-shot Neural Backdoor Erasing via Adversarial Weight Masking},
author={Shuwen Chai and Jinghui Chen},
booktitle={Thirty-Sixth Conference on Neural Information Processing Systems},
year={2022}
}