Attack by Yourself: Effective and Unnoticeable Multi-Category Graph Backdoor Attacks with Subgraph Triggers Pool

• URL: http://arxiv.org/abs/2412.17213v1
• Date: Mon, 23 Dec 2024 01:52:15 GMT
• Title: Attack by Yourself: Effective and Unnoticeable Multi-Category Graph Backdoor Attacks with Subgraph Triggers Pool
• Authors: Jiangtong Li, Dungy Liu, Dawei Cheng, Changchun Jiang,
• Abstract summary: Recent researches highlight their vulnerability to backdoor attacks in node classification.<n> adaptive trigger generators typically have a simple structure, limited parameters, and lack category-aware graph knowledge.<n>We propose a novel approach for textbfEffective and textbfUnnoticeable textbfMulti-textbfCategory(EUMC)
• Score: 13.808863632701609
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: \textbf{G}raph \textbf{N}eural \textbf{N}etworks~(GNNs) have achieved significant success in various real-world applications, including social networks, finance systems, and traffic management. Recent researches highlight their vulnerability to backdoor attacks in node classification, where GNNs trained on a poisoned graph misclassify a test node only when specific triggers are attached. These studies typically focus on single attack categories and use adaptive trigger generators to create node-specific triggers. However, adaptive trigger generators typically have a simple structure, limited parameters, and lack category-aware graph knowledge, which makes them struggle to handle backdoor attacks across multiple categories as the number of target categories increases. We address this gap by proposing a novel approach for \textbf{E}ffective and \textbf{U}nnoticeable \textbf{M}ulti-\textbf{C}ategory~(EUMC) graph backdoor attacks, leveraging subgraph from the attacked graph as category-aware triggers to precisely control the target category. To ensure the effectiveness of our method, we construct a \textbf{M}ulti-\textbf{C}ategory \textbf{S}ubgraph \textbf{T}riggers \textbf{P}ool~(MC-STP) using the subgraphs of the attacked graph as triggers. We then exploit the attachment probability shifts of each subgraph trigger as category-aware priors for target category determination. Moreover, we develop a ``select then attach'' strategy that connects suitable category-aware trigger to attacked nodes for unnoticeability. Extensive experiments across different real-world datasets confirm the efficacy of our method in conducting multi-category graph backdoor attacks on various GNN models and defense strategies.

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