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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 / No. 11: AAAI-21 Technical Tracks 11

Uncertainty-Matching Graph Neural Networks to Defend Against Poisoning Attacks

February 1, 2023

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Authors

Uday Shankar Shanthamallu

Arizona State University


Jayaraman J. Thiagarajan

Lawrence Livermore National Labs


Andreas Spanias

Arizona State University


DOI:

10.1609/aaai.v35i11.17147


Abstract:

Graph Neural Networks (GNNs), a generalization of neural networks to graph-structured data, are often implemented using message passes between entities of a graph. While GNNs are effective for node classification, link prediction and graph classification, they are vulnerable to adversarial attacks, i.e., a small perturbation to the structure can lead to a non-trivial performance degradation. In this work, we propose Uncertainty Matching GNN (UM-GNN), that is aimed at improving the robustness of GNN models, particularly against poisoning attacks to the graph structure, by leveraging epistemic uncertainties from the message passing framework. More specifically, we propose to build a surrogate predictor that does not directly access the graph structure, but systematically extracts reliable knowledge from a standard GNN through a novel uncertainty-matching strategy. Interestingly, this uncoupling makes UM-GNN immune to evasion attacks by design, and achieves significantly improved robustness against poisoning attacks. Using empirical studies with standard benchmarks and a suite of global and target attacks, we demonstrate the effectiveness of UM-GNN, when compared to existing baselines including the state-of-the-art robust GCN.

Topics: AAAI

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HOW TO CITE:

Uday Shankar Shanthamallu||Jayaraman J. Thiagarajan||Andreas Spanias Uncertainty-Matching Graph Neural Networks to Defend Against Poisoning Attacks Proceedings of the AAAI Conference on Artificial Intelligence (2021) 9524-9532.

Uday Shankar Shanthamallu||Jayaraman J. Thiagarajan||Andreas Spanias Uncertainty-Matching Graph Neural Networks to Defend Against Poisoning Attacks AAAI 2021, 9524-9532.

Uday Shankar Shanthamallu||Jayaraman J. Thiagarajan||Andreas Spanias (2021). Uncertainty-Matching Graph Neural Networks to Defend Against Poisoning Attacks. Proceedings of the AAAI Conference on Artificial Intelligence, 9524-9532.

Uday Shankar Shanthamallu||Jayaraman J. Thiagarajan||Andreas Spanias. Uncertainty-Matching Graph Neural Networks to Defend Against Poisoning Attacks. Proceedings of the AAAI Conference on Artificial Intelligence 2021 p.9524-9532.

Uday Shankar Shanthamallu||Jayaraman J. Thiagarajan||Andreas Spanias. 2021. Uncertainty-Matching Graph Neural Networks to Defend Against Poisoning Attacks. "Proceedings of the AAAI Conference on Artificial Intelligence". 9524-9532.

Uday Shankar Shanthamallu||Jayaraman J. Thiagarajan||Andreas Spanias. (2021) "Uncertainty-Matching Graph Neural Networks to Defend Against Poisoning Attacks", Proceedings of the AAAI Conference on Artificial Intelligence, p.9524-9532

Uday Shankar Shanthamallu||Jayaraman J. Thiagarajan||Andreas Spanias, "Uncertainty-Matching Graph Neural Networks to Defend Against Poisoning Attacks", AAAI, p.9524-9532, 2021.

Uday Shankar Shanthamallu||Jayaraman J. Thiagarajan||Andreas Spanias. "Uncertainty-Matching Graph Neural Networks to Defend Against Poisoning Attacks". Proceedings of the AAAI Conference on Artificial Intelligence, 2021, p.9524-9532.

Uday Shankar Shanthamallu||Jayaraman J. Thiagarajan||Andreas Spanias. "Uncertainty-Matching Graph Neural Networks to Defend Against Poisoning Attacks". Proceedings of the AAAI Conference on Artificial Intelligence, (2021): 9524-9532.

Uday Shankar Shanthamallu||Jayaraman J. Thiagarajan||Andreas Spanias. Uncertainty-Matching Graph Neural Networks to Defend Against Poisoning Attacks. AAAI[Internet]. 2021[cited 2023]; 9524-9532.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
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