Findings from Shanghai University Broaden Understanding of Engineering (A Semantic Backdoor Attack Against Graph Convolutional Networks).

New research from Shanghai University explores a new type of threat called a backdoor attack on graph convolutional networks (GCNs), which are used for graph-related tasks. The study focuses on semantic backdoor attacks, where a naturally occurring semantic feature of samples can serve as a trigger to misclassify testing samples. The researchers propose a semantic backdoor attack against GCNs (SBAG) and evaluate its effectiveness on five graph datasets. The experimental results show that SBAG can achieve high attack success rates on both unmodified and modified testing samples. This research provides insights into the security vulnerabilities of GCNs. [Extracted from the article]