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Backdoor attacks in the traditional graph neural networks (GNNs) field areeasily detectable due to the dilemma of confusing labels. To explore thebackdoor vulnerability of GNNs and create a more stealthy backdoor attackmethod, a clean-label graph backdoor attack method(CGBA) in the nodeclassification task is proposed in this paper. Differently from existingbackdoor attack methods, CGBA requires neither modification of node labels norgraph structure. Specifically, to solve the problem of inconsistency betweenthe contents and labels of the samples, CGBA selects poisoning samples in aspecific target class and uses the label of sample as the target label (i.e.,clean-label) after injecting triggers into the target samples. To guarantee thesimilarity of neighboring nodes, the raw features of the nodes are elaboratelypicked as triggers to further improve the concealment of the triggers.Extensive experiments results show the effectiveness of our method. When thepoisoning rate is 0.04, CGBA can achieve an average attack success rate of87.8%, 98.9%, 89.1%, and 98.5%, respectively.

A clean-label graph backdoor attack method in node classification task