Graph Neural Networks (GNNs) are recognized as potent tools for processing
real-world data organized in graph structures. Especially inductive GNNs, which
enable the processing of graph-structured data without relying on predefined
graph structures, are gaining importance in an increasingly wide variety of
applications. As these networks demonstrate proficiency across a range of
tasks, they become lucrative targets for model-stealing attacks where an
adversary seeks to replicate the functionality of the targeted network. A large
effort has been made to develop model-stealing attacks that focus on models
trained with images and texts. However, little attention has been paid to GNNs
trained on graph data. This paper introduces a novel method for unsupervised
model-stealing attacks against inductive GNNs, based on graph contrasting
learning and spectral graph augmentations to efficiently extract information
from the target model. The proposed attack is thoroughly evaluated on six
datasets. The results show that this approach demonstrates a higher level of
efficiency compared to existing stealing attacks. More concretely, our attack
outperforms the baseline on all benchmarks achieving higher fidelity and
downstream accuracy of the stolen model while requiring fewer queries sent to
the target model.

该论文介绍了一种针对归纳式图神经网络的无监督模型窃取攻击的新方法，该方法基于图对比学习和谱图增强，通过从目标模型高效提取信息。在六个数据集上对该攻击进行了全面评估，结果表明该方法相较于现有的窃取攻击具有更高的效率，并在所有基准测试中优于基准测试，实现了更高的窃取模型的保真度和下游准确性，同时对目标模型发送的查询次数较少。

基于图神经网络的高效模型窃取攻击

Efficient Model-Stealing Attacks Against Inductive Graph Neural Networks

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.

本文提出了针对归纳图神经网络的模型盗窃攻击，定义了威胁模型并根据对手的背景知识和目标模型的响应提出了六种攻击方式。在六个基准数据集上的评估结果显示，该攻击方式取得了良好的性能表现。