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Computer Science > Machine Learning
Title: A General Black-box Adversarial Attack on Graph-based Fake News Detectors
(Submitted on 24 Apr 2024 (v1), last revised 26 Apr 2024 (this version, v2))
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. Specifically, as sharing is an important social interaction for GNN-based fake news detectors to construct the graph, we simulate sharing behaviors to fool the detectors. Firstly, we propose a fraudster selection module to select engaged users leveraging local and global information. In addition, a post injection module guides the selected users to create shared relations by sending posts. The sharing records will be added to the social context, leading to a general attack against different detectors. Experimental results on empirical datasets demonstrate the effectiveness of GAFSI.
Submission history
From: Zechen Pan [view email][v1] Wed, 24 Apr 2024 09:04:05 GMT (1912kb,D)
[v2] Fri, 26 Apr 2024 01:52:38 GMT (1912kb,D)
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