  Graph Neural Networks (GNNs) have gained popularity in numerous domains, yet
they are vulnerable to backdoor attacks that can compromise their performance
and ethical application. The detection of these attacks is crucial for
maintaining the reliability and security of GNN classification tasks, but
effective detection techniques are lacking. Following an initial investigation,
we observed that while graph-level explanations can offer limited insights,
their effectiveness in detecting backdoor triggers is inconsistent and
incomplete. To bridge this gap, we extract and transform secondary outputs of
GNN explanation mechanisms, designing seven novel metrics that more effectively
detect backdoor attacks. Additionally, we develop an adaptive attack to
rigorously evaluate our approach. We test our method on multiple benchmark
datasets and examine its efficacy against various attack models. Our results
show that our method can achieve high detection performance, marking a
significant advancement in safeguarding GNNs against backdoor attacks.
