Graph Backdoor Attack

Graph backdoor attacks exploit vulnerabilities in graph neural networks (GNNs), such as those used in recommendation systems and social network analysis, by injecting malicious triggers into training data to manipulate model predictions. Current research focuses on developing more effective and stealthy attacks, including those that modify only labels or leverage graph condensation techniques, as well as designing robust defenses that can identify and mitigate these attacks without compromising model accuracy. Understanding and addressing these attacks is crucial for ensuring the security and reliability of GNNs in various real-world applications.

Papers