A General Black-box Adversarial Attack on Graph-based Fake News Detectors
<|reference_start|>A General Black-box Adversarial Attack on Graph-based Fake News Detectors: 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.<|reference_end|>
@article{zhu2024a,
    title={A General Black-box Adversarial Attack on Graph-based Fake News
  Detectors},
    author={Peican Zhu, Zechen Pan, Yang Liu, Jiwei Tian, Keke Tang, Zhen Wang},
    journal={arXiv preprint arXiv:2404.15744},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.15744},
    primaryClass={cs.LG cs.AI cs.CR}
}