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September 16, 2025ElectronicsOpen Access

Boosting Clean-Label Backdoor Attacks on Graph Classification

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This research demonstrates enhanced backdoor attack strategies, improving attack rates in graph networks while managing security implications.

Adding strategically positioned trigger nodes boosts backdoor attack effectiveness in graph neural networks.
Adversarial perturbations previously successful in other domains fail due to the unique message-passing dynamics of GNNs.
The proposed method improves attack success rates while minimizing drops in clean classification accuracy.
Experimentation on datasets like NCI1 and ENZYMES reveals significant enhancements over existing methodologies.

Wang et al. (2025) studied this question.

View Full Paper

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September 16, 2025ElectronicsOpen Access

View Full Paper

Ask AI

Bookmark

Authors

This research demonstrates enhanced backdoor attack strategies, improving attack rates in graph networks while managing security implications.

Adding strategically positioned trigger nodes boosts backdoor attack effectiveness in graph neural networks.
Adversarial perturbations previously successful in other domains fail due to the unique message-passing dynamics of GNNs.
The proposed method improves attack success rates while minimizing drops in clean classification accuracy.
Experimentation on datasets like NCI1 and ENZYMES reveals significant enhancements over existing methodologies.

Wang et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark