Backdoor Attacks to Graph Neural Networks
A PyTorch implementation of "Backdoor Attacks to Graph Neural Networks" (SACMAT'21) [paper] [arxiv]
The code is based on the Pytorch implementation of [GIN]
Abstract
In this work, we propose the first backdoor attack to graph neural networks (GNN). Specifically, we propose a subgraph based backdoor attack to GNN for graph classification. In our backdoor attack, a GNN classifier predicts an attacker-chosen target label for a testing graph once a predefined subgraph is injected to the testing graph. Our empirical results on three real-world graph datasets show that our backdoor attacks are effective with a small impact on a GNN’s prediction accuracy for clean testing graphs. Moreover, we generalize a randomized smoothing based certified defense to defend against our backdoor attacks. Our empirical results show that the defense is effective in some cases but ineffective in other cases, highlighting the needs of new defenses for our backdoor attacks.
Requirements
matplotlib==3.1.1
numpy==1.17.1
torch==1.2.0
scipy==1.3.1
networkx==2.4
tqdm==4.47.0
pickle==0.7.5
Run the code
You can clone this repository and run the code
git clone https://github.com/zaixizhang/graphbackdoor.git
cd graphbackdoor
unzip dataset.zip
sh train.sh
Cite
If you find this repo to be useful, please cite our paper. Thank you.
@inproceedings{10.1145/3450569.3463560,
author = {Zhang, Zaixi and Jia, Jinyuan and Wang, Binghui and Gong, Neil Zhenqiang},
title = {Backdoor Attacks to Graph Neural Networks},
url = {https://doi.org/10.1145/3450569.3463560},
doi = {10.1145/3450569.3463560},
series = {SACMAT '21}
}