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GUARD: Graph Universal Adversarial Defense. (arXiv:2204.09803v1 [cs.LG])
April 22, 2022, 1:20 a.m. | Jintang Li, Jie Liao, Ruofan Wu, Liang Chen, Changhua Meng, Zibin Zheng, Weiqiang Wang
cs.CR updates on arXiv.org arxiv.org
Recently, graph convolutional networks (GCNs) have shown to be vulnerable to
small adversarial perturbations, which becomes a severe threat and largely
limits their applications in security-critical scenarios. To mitigate such a
threat, considerable research efforts have been devoted to increasing the
robustness of GCNs against adversarial attacks. However, current approaches for
defense are typically designed for the whole graph and consider the global
performance, posing challenges in protecting important local nodes from
stronger adversarial targeted attacks. In this work, we …
More from arxiv.org / cs.CR updates on arXiv.org
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