Graph Neural Backdoor: Fundamentals, Methodologies, Applications, and Future Directions

TL;DR

This paper surveys the emerging field of GNN backdoors, discussing their fundamentals, attack and defense methodologies, applications, and future research directions to enhance understanding and security.

Contribution

It is the first comprehensive survey on GNN backdoors, categorizing existing attacks and defenses, and analyzing their applicability and future research avenues.

Findings

Categorizes GNN backdoor attack methods and defenses.

Analyzes the application scenarios and effectiveness of backdoors.

Identifies future research directions in GNN security.

Abstract

Graph Neural Networks (GNNs) have significantly advanced various downstream graph-relevant tasks, encompassing recommender systems, molecular structure prediction, social media analysis, etc. Despite the boosts of GNN, recent research has empirically demonstrated its potential vulnerability to backdoor attacks, wherein adversaries employ triggers to poison input samples, inducing GNN to adversary-premeditated malicious outputs. This is typically due to the controlled training process, or the deployment of untrusted models, such as delegating model training to third-party service, leveraging external training sets, and employing pre-trained models from online sources. Although there's an ongoing increase in research on GNN backdoors, comprehensive investigation into this field is lacking. To bridge this gap, we propose the first survey dedicated to GNN backdoors. We begin by outlining the fundamental definition of GNN, followed by the detailed summarization and categorization of current GNN backdoor attacks and defenses based on their technical characteristics and application scenarios. Subsequently, the analysis of the applicability and use cases of GNN backdoors is undertaken. Finally, the exploration of potential research directions of GNN backdoors is presented. This survey aims to explore the principles of graph backdoors, provide insights to defenders, and promote future security research.