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Backdoor Attacks on Graph Classification via Data Augmentation and Dynamic Poisoning

delete2026-01-01
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PRE
AI
Y
Yadong Wang
Z
Zhiwei Zhang *
P
Pengpeng Qiao
Y
Ye Yuan
王
王国仁 (Guoren Wang)
DOI：10.1007/978-3-032-06066-2_16delete
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摘要

摘要

En 中文
图神经网络（GNNs）在生物信息学、社交网络和化学信息学等领域已得到广泛应用，但它们仍然容易受到后门攻击。现有的后门攻击通常依赖子图触发器，这往往会引入可检测的异常，并采用随机中毒样本选择，导致隐蔽性和效率降低。为解决这些局限性，我们提出了一种新型后门攻击框架，利用基于数据增强的触发器和动态中毒样本选择。具体而言，我们设计了三种替代数据增强策略，即基于余弦相似性的边修改、基于度中心性的边移除和通过梯度显著性进行特征掩码，作为后门触发器。此外，我们引入了一种受遗忘事件启发的动态中毒样本选择方法。该方法动态优先考虑高影响中毒样本，以提高攻击效率，同时减少达到相应攻击成功率（ASR）所需的样本数量。在四个基准数据集PROTEINS、NCI1、Mutagenicity和ENZYMES上的实验证明了我们方法的优势。
Keyword:
GNNs
Graph Classification
Backdoor Attacks

期刊

M
MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES. RESEARCH TRACK, ECML PKDD 2025, PT III
IF:
0
论文数:
30
被引数:
0

机构

B
beijing institute of technology
学者数:
5.5W
论文数: 4.0W
被引数: 63
I
institute of science tokyo
学者数:
3.5K
论文数: 1.3K
被引数: 0
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