3 papers
cs.LG2025
JANUS: A Dual-Constraint Generative Framework for Stealthy Node Injection Attacks
Jiahao Zhang, Xiaobing Pei, Zhaokun Zhong +2
Graph Neural Networks (GNNs) have demonstrated remarkable performance across various applications, yet they are vulnerable to sophisticated adversarial attacks, particularly node i…
cs.LG2025
Revisiting the Relationship between Adversarial and Clean Training: Why Clean Training Can Make Adversarial Training Better
MingWei Zhou, Xiaobing Pei
Adversarial training (AT) is an effective technique for enhancing adversarial robustness, but it usually comes at the cost of a decline in generalization ability. Recent studies ha…
cs.LG2024
AHSG: Adversarial Attack on High-level Semantics in Graph Neural Networks
Kai Yuan, Jiahao Zhang, Yidi Wang +1
Adversarial attacks on Graph Neural Networks aim to perturb the performance of the learner by carefully modifying the graph topology and node attributes. Existing methods achieve a…