BackdoorBench: a comprehensive benchmark of backdoor attack and defense methods

Website • Paper • Leaderboard

BackdoorBench is a comprehensive benchmark of backdoor learning, which studies the adversarial vulnerablity of deep learning models in the training stage. It aims to provide easy implementations of mainstream backdoor attack and defense methods.

❗V2.0 Updates

✅ Correction:

1. Attack : Fix the bug in Label Consistent attack method, in v1.0 version, poisoned data only add adversarial noise without square trigger, which is not consistent with the paper.

✅ Code:

1. Structure : Warp attack methods and defense methods into classes and reduce replicated code.
2. Dataset Processing : Update bd_dataset into bd_dataset_v2, which can handle large scale dataset more efficently.
3. Poison Data Generation : Provide necessary code to generate poisoned dataset for attack methods (see ./resource folder, we have seperate readme files).
4. Models : We add VGG19_bn, ConvNeXT_tiny, ViT_B_16.

✅ Methods:

1. Attack :Add 4 new attack methods: Blind, BPP, LIRA, TrojanNN. (Totally 12 attack methods now).
2. Defense :Add 6 new defense methods: CLP, D-BR, D-ST, EP, I-BAU, BNP. (Totally 15 defense methods now).

✅ Analysis Tools :

1. Data Analysis : Add 2 new methods: UMAP, Image Quality
2. Models Analysis : Add 9 new methods: Activated Image, Feature Visualization, Feature Map, Activation Distribution, Trigger Activation Change, Lipschitz Constant, Loss Landscape, Network Structure, Eigenvalues of Hessian
3. Evaluation Analysis : Add 2 new methods: Confusion Matrix, Metric

🔲 Comprehensive evaluations will be coming soon...

❗ For V1.0 please check here

• Features

• Installation

• Quick Start

  • Attack

  • Defense

• Supported attacks

• Supported defenses

• Analysis Tools

• Citation

• Copyright

Features

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BackdoorBench has the following features:

⭐️ Methods:

• 12 Backdoor attack methods: BadNets, Blended, Blind, BPP, Input-aware, Label Consistent, Low Frequency, LIRA, SIG, SSBA, TrojanNN, WaNet
• 15 Backdoor defense methods: FT, Spectral, AC, FP, ABL, NAD, NC, DBD, ANP,CLP, D-BR, D-ST, EP, I-BAU, BNP

⭐️ Datasets: CIFAR-10, CIFAR-100, GTSRB, Tiny ImageNet

⭐️ Models: PreAct-Resnet18, VGG19_bn, ConvNeXT_tiny, ViT_B_16, VGG19, DenseNet-161, MobileNetV3-Large, EfficientNet-B3

⭐️ Learboard: We provide a public leaderboard of evaluating all backdoor attacks against all defense methods.

BackdoorBench will be continuously updated to track the lastest advances of backddor learning. The implementations of more backdoor methods, as well as their evaluations are on the way. You are welcome to contribute your backdoor methods to BackdoorBench.

Installation

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You can run the following script to configurate necessary environment

git clone [email protected]:SCLBD/BackdoorBench.git
cd BackdoorBench
conda create -n backdoorbench python=3.8
conda activate backdoorbench
sh ./sh/install.sh
sh ./sh/init_folders.sh

Quick Start

Attack

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This is a example for BadNets

1. Generate trigger

If you want to change the trigger for BadNets, you should go to the ./resource/badnet, and follow the readme there to generate new trigger pattern.

python ./resource/badnet/generate_white_square.py --image_size 32 --square_size 3 --distance_to_right 0 --distance_to_bottom 0 --output_path ./resource/badnet/trigger_image.png

Note that for data-poisoning-based attacks (BadNets, Blended, Label Consistent, Low Frequency, SSBA). Our scripts in ./attack are just for training, they do not include the data generation process.(Because they are time-comsuming, and we do not want to waste your time.) You should go to the ./resource folder to generate the trigger for training.

2. Backdoor training

python ./attack/badnet.py --yaml_path ../config/attack/prototype/cifar10.yaml --patch_mask_path ../resource/badnet/trigger_image.png  --save_folder_name badnet_0_1

After attack you will get a folder with all files saved in ./record/<folder name in record>, including attack_result.pt for attack model and backdoored data, which will be used by following defense methods. If you want to change the args, you can both specify them in command line and in corresponding YAML config file (eg. default.yaml).(They are the defaults we used if no args are specified in command line.) The detailed descriptions for each attack may be put into the add_args function in each script.

Note that for some attacks, they may need pretrained models to generate backdoored data. For your ease, we provide various data/trigger/models we generated in google drive. You can download them at here

Defense

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This is a demo script of running abl defense on cifar-10 for badnet attack. Before defense you need to run badnet attack on cifar-10 at first. Then you use the folder name as result_file.

python ./defense/abl.py --result_file badnet_0_1 --yaml_path ./config/defense/abl/cifar10.yaml --dataset cifar10

If you want to change the args, you can both specify them in command line and in corresponding YAML config file (eg. default.yaml).(They are the defaults we used if no args are specified in command line.) The detailed descriptions for each attack may be put into the add_args function in each script.

Supported attacks

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Supported defenses

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Analysis Tools

Citation

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If interested, you can read our recent works about backdoor learning, and more works about trustworthy AI can be found here.

@inproceedings{backdoorbench,
  title={BackdoorBench: A Comprehensive Benchmark of Backdoor Learning},
  author={Wu, Baoyuan and Chen, Hongrui and Zhang, Mingda and Zhu, Zihao and Wei, Shaokui and Yuan, Danni and Shen, Chao},
  booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
  year={2022}
}

@article{wu2023adversarial,
  title={Adversarial Machine Learning: A Systematic Survey of Backdoor Attack, Weight Attack and Adversarial Example},
  author={Wu, Baoyuan and Liu, Li and Zhu, Zihao and Liu, Qingshan and He, Zhaofeng and Lyu, Siwei},
  journal={arXiv preprint arXiv:2302.09457},
  year={2023}
}

@article{cheng2023tat,
  title={TAT: Targeted backdoor attacks against visual object tracking},
  author={Cheng, Ziyi and Wu, Baoyuan and Zhang, Zhenya and Zhao, Jianjun},
  journal={Pattern Recognition},
  volume={142},
  pages={109629},
  year={2023},
  publisher={Elsevier}
}

@inproceedings{sensitivity-backdoor-defense-nips2022,
 title = {Effective Backdoor Defense by Exploiting Sensitivity of Poisoned Samples},
 author = {Chen, Weixin and Wu, Baoyuan and Wang, Haoqian},
 booktitle = {Advances in Neural Information Processing Systems},
 volume = {35},
 pages = {9727--9737},
 year = {2022}
}

@inproceedings{dbd-backdoor-defense-iclr2022,
    title={Backdoor Defense via Decoupling the Training Process},
    author={Huang, Kunzhe and Li, Yiming and Wu, Baoyuan and Qin, Zhan and Ren, Kui},
    booktitle={International Conference on Learning Representations},
    year={2022}
}

@inproceedings{ssba-backdoor-attack-iccv2021,
    title={Invisible backdoor attack with sample-specific triggers},
    author={Li, Yuezun and Li, Yiming and Wu, Baoyuan and Li, Longkang and He, Ran and Lyu, Siwei},
    booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
    pages={16463--16472},
    year={2021}
}

Copyright

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This repository is licensed by The Chinese University of Hong Kong, Shenzhen and Shenzhen Research Institute of Big Data under Creative Commons Attribution-NonCommercial 4.0 International Public License (identified as CC BY-NC-4.0 in SPDX). More details about the license could be found in LICENSE.

This project is built by the Secure Computing Lab of Big Data (SCLBD) at The Chinese University of Hong Kong, Shenzhen and Shenzhen Research Institute of Big Data, directed by Professor Baoyuan Wu. SCLBD focuses on research of trustworthy AI, including backdoor learning, adversarial examples, federated learning, fairness, etc.

If any suggestion or comment, please contact us at [email protected].