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。