Graph Neural Networks (GNNs) have achieved promising results in tasks such as
node classification and graph classification. However, recent studies reveal
that GNNs are vulnerable to backdoor attacks, posing a significant threat to
their real-world adoption. Despite initial efforts to defend against specific
graph backdoor attacks, there is no work on defending against various types of
backdoor attacks where generated triggers have different properties. Hence, we
first empirically verify that prediction variance under edge dropping is a
crucial indicator for identifying poisoned nodes. With this observation, we
propose using random edge dropping to detect backdoors and theoretically show
that it can efficiently distinguish poisoned nodes from clean ones.
Furthermore, we introduce a novel robust training strategy to efficiently
counteract the impact of the triggers. Extensive experiments on real-world
datasets show that our framework can effectively identify poisoned nodes,
significantly degrade the attack success rate, and maintain clean accuracy when
defending against various types of graph backdoor attacks with different
properties.

图神经网络对抵御不同属性的后门攻击进行了实证验证，并提出了一种随机边缘去除的检测方法，并通过引入新的强化训练策略来有效地抵御触发器的影响。在真实数据集上进行的广泛实验证明，该框架能够有效地识别受到污染的节点，在抵御具有不同属性的不同类型图后门攻击时显著降低攻击成功率，并保持清洁准确性。