A General Black-box Adversarial Attack on Graph-based Fake News Detectors

arXiv:2404.15744v1 Announce Type: cross
Abstract: Graph Neural Network (GNN)-based fake news detectors apply various methods to construct graphs, aiming to learn distinctive news embeddings for classification. Since the construction details are unknown for attackers in a black-box scenario, it is unrealistic to conduct the classical adversarial attacks that require a specific adjacency matrix. In this paper, we propose the first general black-box adversarial attack framework, i.e., General Attack via Fake Social Interaction (GAFSI), against detectors based on different graph structures. …

adversarial adversarial attack adversarial attacks arxiv attack attackers attacks box classification construction cs.ai cs.cr cs.lg fake fake news general graph graphs learn network neural network scenario