Summary of Efficient Model-stealing Attacks Against Inductive Graph Neural Networks, by Marcin Podhajski et al.
Efficient Model-Stealing Attacks Against Inductive Graph Neural Networks
by Marcin Podhajski, Jan Dubiński, Franziska Boenisch, Adam Dziedzic, Agnieszka Pregowska, Tomasz P. Michalak
First submitted to arxiv on: 20 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers introduce a novel method for stealing graph neural networks (GNNs) trained on graph-structured data without relying on predefined graph structures. The proposed attack, which utilizes graph contrastive learning and spectral graph augmentations, is designed to efficiently extract information from the targeted model without requiring any labeled data. The approach is thoroughly evaluated on six datasets, demonstrating superior performance compared to the current state-of-the-art method by Shen et al. (2021). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows that it’s possible to steal GNNs trained on graph data without needing lots of labeled information. The researchers developed a new way to do this using techniques like graph contrastive learning and spectral graph augmentations. They tested their approach on six different datasets and found that it works better than previous methods. |
