Backdoor attacks pose a significant security risk to graph learning models.
Backdoors can be embedded into the target model by inserting backdoor triggers
into the training dataset, causing the model to make incorrect predictions when
the trigger is present. To counter backdoor attacks, backdoor detection has
been proposed. An emerging detection strategy in the vision and NLP domains is
based on an intriguing phenomenon: when training models on a mixture of
backdoor and clean samples, the loss on backdoor samples drops significantly
faster than on clean samples, allowing backdoor samples to be easily detected
by selecting samples with the lowest loss values. However, the ignorance of
topological feature information on graph data limits its detection
effectiveness when applied directly to the graph domain. To this end, we
propose an explanation-guided backdoor detection method to take advantage of
the topological information. Specifically, we train a helper model on the graph
dataset, feed graph samples into the model, and then adopt explanation methods
to attribute model prediction to an important subgraph. We observe that
backdoor samples have distinct attribution distribution than clean samples, so
the explanatory subgraph could serve as more discriminative features for
detecting backdoor samples. Comprehensive experiments on multiple popular
datasets and attack methods demonstrate the effectiveness and explainability of
our method. Our code is available:
this https URL

我们提出了一种基于解释的后门检测方法，利用拓扑信息来检测后门样本，通过训练一个辅助模型，将图样本输入模型，并采用解释方法将模型预测归因于一个重要的子图，通过观察到后门样本和干净样本的不同归因分布，解释子图可以作为更具有辨别性的特征来检测后门样本，全面的实验验证了我们方法的有效性和可解释性。