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Model Stealing Attacks Against Inductive Graph Neural Networks

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conference contribution
posted on 2023-11-29, 18:20 authored by Yun Shen, xinlei.he, Yufei Han, Yang ZhangYang Zhang
Many real-world data come in the form of graphs. Graph neural networks (GNNs), a new family of machine learning (ML) models, have been proposed to fully leverage graph data to build powerful applications. In particular, the inductive GNNs, which can generalize to unseen data, become mainstream in this direction. Machine learning models have shown great potential in various tasks and have been deployed in many real-world scenarios. To train a good model, a large amount of data as well as computational resources are needed, leading to valuable intellectual property. Previous research has shown that ML models are prone to model stealing attacks, which aim to steal the functionality of the target models. However, most of them focus on the models trained with images and texts. On the other hand, little attention has been paid to models trained with graph data, i.e., GNNs. In this paper, we fill the gap by proposing the first model stealing attacks against inductive GNNs. We systematically define the threat model and propose six attacks based on the adversary’s background knowledge and the responses of the target models. Our evaluation on six benchmark datasets shows that the proposed model stealing attacks against GNNs achieve promising performance。


Preferred Citation

Yun Shen, Xinlei He, Yufei Han and Yang Zhang. Model Stealing Attacks Against Inductive Graph Neural Networks. In: IEEE Symposium on Security and Privacy (S&P). 2022.

Primary Research Area

  • Trustworthy Information Processing

Name of Conference

IEEE Symposium on Security and Privacy (S&P)

Legacy Posted Date


Open Access Type

  • Unknown


@inproceedings{cispa_all_3638, title = "Model Stealing Attacks Against Inductive Graph Neural Networks", author = "Shen, Yun and He, Xinlei and Han, Yufei and Zhang, Yang", booktitle="{IEEE Symposium on Security and Privacy (S&P)}", year="2022", }

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