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 present a simple yet effective method, named
Graph Universal Adversarial Defense (GUARD). Unlike previous works, GUARD
protects each individual node from attacks with a universal defensive patch,
which is generated once and can be applied to any node (node-agnostic) in a
graph. Extensive experiments on four benchmark datasets demonstrate that our
method significantly improves robustness for several established GCNs against
multiple adversarial attacks and outperforms state-of-the-art defense methods
by large margins. Our code is publicly available at
this https URL.

本研究提出了一种基于 Graph Universal Adversarial Defense (GUARD) 的简单而有效的方法，通过生成一个通用防御补丁来保护每个节点免受攻击，并在多个对抗攻击中显著提高了已建立的 GCN 的鲁棒性，并且该方法超越了目前的最先进的防御方法。