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A General Black-box Adversarial Attack on Graph-based Fake News Detectors
April 25, 2024, 7:42 p.m. | Peican Zhu, Zechen Pan, Yang Liu, Jiwei Tian, Keke Tang, Zhen Wang
cs.LG updates on arXiv.org arxiv.org
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. …
abstract adversarial adversarial attacks apply arxiv attacks box classification construct construction cs.ai cs.cr cs.lg detectors embeddings fake fake news general gnn graph graph-based graph neural network graphs learn network neural network type
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