@inproceedings{
cao2024data,
title={Data Free Backdoor Attacks},
author={Bochuan Cao and Jinyuan Jia and Chuxuan Hu and Wenbo Guo and Zhen Xiang and Jinghui Chen and Bo Li and Dawn Song},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=pX71TM2MLh}
}
Backdoor attacks aim to inject a backdoor into a classifier such that it predicts any input with an attacker-chosen backdoor trigger as an attacker-chosen target class. 
Existing backdoor attacks require either retraining the classifier with some clean data or modifying the model's architecture.
As a result, they are 1) not applicable when clean data is unavailable, 2) less efficient when the model is large, and 3) less stealthy due to architecture changes. 
In this work, we propose DFBA, a novel retraining-free and data-free backdoor attack without changing the model architecture. 
Technically, our proposed method modifies a few parameters of a classifier to inject a backdoor. 
Through theoretical analysis, we verify that our injected backdoor is provably undetectable and unremovable by various state-of-the-art defenses under mild assumptions. 
Our evaluation on multiple datasets further demonstrates that our injected backdoor: 1) incurs negligible classification loss, 2) achieves 100\% attack success rates, and 3) bypasses six existing state-of-the-art defenses. 
Moreover, our comparison with a state-of-the-art non-data-free backdoor attack shows our attack is more stealthy and effective against various defenses while achieving less classification accuracy loss.
We will release our code upon paper acceptance.
Data Free Backdoor Attacks
Bochuan Cao Jinyuan Jia Chuxuan Hu Wenbo Guo Zhen Xiang Jinghui Chen Bo Li Dawn Song