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

TL;DR

This paper introduces GAFSI, a novel black-box adversarial attack framework that manipulates social sharing behaviors to deceive GNN-based fake news detectors without needing detailed graph information.

Contribution

It presents the first general black-box attack method targeting various GNN-based fake news detectors by simulating social sharing behaviors.

Findings

GAFSI effectively fools different fake news detectors.

The attack significantly reduces detection accuracy.

Experimental results validate the attack's generality and effectiveness.

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.