Awesome

DBD

This a Pytorch implementation of our paper "Backdoor Defense via Decoupling the Training Process".

Table of Contents:

• DBD
  • Setup
    • Environments
    • Datasets
    • Log and checkpoint directories
  • Usage
    • No Defense
    • DBD
  • Pretrained Models
    • Test
    • Results
• License
• Citation

Setup

Environments

We recommend conda as the package manager to setup the environment used in our experiments. Create the environment dbd from the environment.yml file and activate it:

conda env create -f environment.yml && conda activate dbd

Datasets

Download CIFAR-10 dataset from its official website and extract it to dataset_dir specified in the YAML configuration files.

Note: Make sure dataset_dir contains the sub-string cifar.

Log and checkpoint directories

Create saved_dir and storage_dir specified in the YAML configuration files to save logs and checkpoints respectively:

mkdir saved_data && mkdir storage

Usage

We give examples to compare the standard supervised training (No Defense) and DBD on CIFAR-10 dataset under BadNets attack with ResNet-18. Other settings can also be found in the YAML configuration files. Please have an overview before running the codes.

No Defense

Run the following script to train a backdoored model:

python supervise.py --config config/supervise/badnets/cifar10_resnet18/example.yaml \
                    --resume False \
                    --gpu 0

DBD

1. Self-Supervised Learning

   Run the following script to train a purified feature extractor:

   python simclr.py --config config/defense/simclr/badnets/cifar10_resnet18/example.yaml \
                    --resume False \
                    --gpu 0
   

2. Semi-Supervised Fine-tuning

   Run the following script to finetune a clean model:

   python mixmatch_finetune.py --config config/defense/mixmatch_finetune/badnets/cifar10_resnet18/example.yaml \
                               --resume False \
                               --gpu 0
   

Pretrained Models

We provide pretrained models here.

Test

Run the following script to test No Defense under BadNets attack:

python test.py --config config/supervise/badnets/cifar10_resnet18/example.yaml \
               --ckpt-dir checkpoint/supervise/badnets/cifar10_resnet18/example \
               --resume latest_model.pt \
               --gpu 0

Run the following script to test DBD under BadNets attack:

python test.py --config config/supervise/badnets/cifar10_resnet18/example.yaml \
               --ckpt-dir checkpoint/defense/mixmatch_finetune/badnets/cifar10_resnet18/example \
               --resume latest_model.pt \
               --gpu 0

Run the following script to test No Defense under Blended attack:

python test.py --config config/supervise/blend/cifar10_resnet18/example.yaml \
               --ckpt-dir checkpoint/supervise/blend/cifar10_resnet18/example \
               --resume latest_model.pt \
               --gpu 0

Run the following script to test DBD under Blended attack:

python test.py --config config/supervise/blend/cifar10_resnet18/example.yaml \
               --ckpt-dir checkpoint/defense/mixmatch_finetune/blend/cifar10_resnet18/example \
               --resume latest_model.pt \
               --gpu 0

Results

License

Our codes is released under GNU General Public License v3.0.

Citation

If our work or this repo is useful for your research, please cite our paper as follows:

@inproceedings{huang2022backdoor,
  title={Backdoor Defense via Decoupling the Training Process},
  author={Kunzhe Huang, Yiming Li, Baoyuan Wu, Zhan Qin and Kui Ren},
  booktitle={ICLR},
  year={2022}
}