Welcome to BackdoorBox (Under Development)

BackdoorBox is a Python toolbox for backdoor attacks and defenses.

This project is still under development and therefore there is no user manual yet. Please refer to the 'tests' sub-folder to get more insights about how to use our implemented methods.

Current Status

Developed Methods

Backdoor Attacks

Method	Source	Key Properties	Note
BadNets	IEEE ACCESS, 2019	poison-only	first backdoor attack
Blended Attack	arXiv, 2017	poison-only, invisible	first invisible attack
Refool (simplified version)	ECCV, 2020	poison-only, sample-specific	first stealthy attack with visible yet natural trigger
Label-consistent Attack	arXiv, 2019	poison-only, invisible, clean-label	first clean-label backdoor attack
ISSBA	ICCV, 2021	poison-only, sample-specific, physical	first poison-only sample-specific attack
WaNet	ICLR, 2021	poison-only, invisible, sample-specific
Blind Backdoor (blended-based)	USENIX Security, 2021	training-controlled	first training-controlled attack targeting loss computation
Input-aware Dynamic Attack	NeurIPS, 2020	training-controlled, optimized, sample-specific	first training-controlled sample-specific attack
Physical Attack	ICLR Workshop, 2021	training-controlled, physical	first physical backdoor attack
LIRA	ICCV, 2021	training-controlled, invisible, optimized, sample-specific

Note: For the convenience of users, all our implemented attacks support obtaining poisoned dataset (via .get_poisoned_dataset()), obtaining infected model (via .get_model()), and training with your own local samples (loaded via torchvision.datasets.DatasetFolder). Please refer to base.py and the attack's codes for more details.

Backdoor Defenses

• ShrinkPad (Key Properties: Pre-processing-based Defense)

Methods Under Development

• TUAP (basic version)
• Sleeper Agent
• NAD
• Fine-tuning
• Pruning
• MCR
• DBD
• SS
• ABL

Contributors

Organization	Contributors
Tsinghua University	Yiming Li, Mengxi Ya, Guanhao Gan, Kuofeng Gao, Xin Yan, Jia Xu, Tong Xu, Sheng Yang, Linghui Zhu, Yang Bai

Citation

If our toolbox is useful for your research, please cite our paper(s) as follows:

@article{li2022backdoorbox,
  title={{BackdoorBox}: A Python Toolbox for Backdoor Learning},
  author={Li, Yiming and Ya, Mengxi and Bai, Yang and Jiang, Yong and Xia, Shu-Tao},
  year={2022}
}

@article{li2020backdoor,
  title={Backdoor Learning: A Survey},
  author={Li, Yiming and Jiang, Yong and Li, Zhifeng and Xia, Shu-Tao},
  journal={arXiv preprint arXiv:2007.08745},
  year={2020}
}