Poster: Multi-target & Multi-trigger Backdoor Attacks on Graph Neural Networks

No ratings

Presented at ACM CCS 2023 by

Jing Xu

Stjepan Picek

Recent research has indicated that Graph Neural Networks (GNNs) are vulnerable to backdoor attacks, and existing studies focus on the One-to-One attack where there is a single target triggered by a single backdoor. In this work, we explore two advanced backdoor attacks, i.e., the multi-target and multi-trigger backdoor attacks, on GNNs: 1) One-to-N attack, where there are multiple backdoor targets triggered by controlling different values of the trigger; 2) N-to-One attack, where the attack is only triggered when all the N triggers are present. The initial experimental results illustrate that both attacks can achieve a high attack success rate (up to 99.72%) on GNNs for the node classification task.