Dual-Targeted adversarial example in evasion attack on graph neural networks.

This study proposes a novel approach for generating dual-targeted adversarial examples in Graph Neural Networks (GNNs), significantly advancing the field of graph-based adversarial attacks. Unlike traditional methods that focus on inducing specific misclassifications in a single model, our approach creates adversarial samples that can simultaneously target multiple models, each inducing distinct misclassifications. This innovation addresses a critical gap in existing techniques by enabling adversarial attacks that are capable of affecting various models with different objectives. We provide a detailed explanation of the method's principles and structure, rigorously evaluate its effectiveness across several GNN models, and visualize the impact using datasets such as Reddit and OGBN-Products. Our contributions highlight the potential for dual-targeted attacks to disrupt GNN performance and emphasize the need for enhanced defensive strategies in graph-based learning systems.
Competing Interests: Declarations. Conflicts of Interest: The authors declare that there are no conflicts of interest regarding the publication of this article. Ethical and informed consent for data used: All authors give ethical and informed consent. There are no human or animal experiments in this paper. Also, there is no copyrighted data related to the figures.
(© 2025. The Author(s).)