{"type":"ScholarlyArticle","title":"A General Black-box Adversarial Attack on Graph-based Fake News Detectors","authors":[{"name":"Peican Zhu","slug":"peican-zhu","given":"Peican","family":"Zhu","affiliation":"School of Artificial Intelligence, Optics and Electronics , Northwestern Polytechnical University"},{"name":"Zechen Pan","slug":"zechen-pan","given":"Zechen","family":"Pan","affiliation":"School of Computer Science , Northwestern Polytechnical University"},{"name":"Yang Liu","slug":"yang-liu","given":"Yang","family":"Liu","affiliation":"School of Artificial Intelligence, Optics and Electronics , Northwestern Polytechnical University"},{"name":"Jiwei Tian","slug":"jiwei-tian","given":"Jiwei","family":"Tian","affiliation":"Air Traffic Control and Navigation College , Air Force Engineering University"},{"name":"Keke Tang","slug":"keke-tang","given":"Keke","family":"Tang","affiliation":"Cyberspace Institute of Advanced Technology , Guangzhou University"},{"name":"Zhen Wang","slug":"zhen-wang","given":"Zhen","family":"Wang","affiliation":"School of Artificial Intelligence, Optics and Electronics , Northwestern Polytechnical University"}],"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.","doi":"10.24963/ijcai.2024/63","url":"https://ijcai.org/proceedings/2024/63","pdf":"https://ijcai.org/proceedings/2024/0063.pdf","markdown":"https://ijcai.org/proceedings/2024/63.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/63","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"AI Ethics, Trust, Fairness","track":"Main Track","number":63,"pages":{"first":568,"last":576},"keywords":["AI Ethics, Trust, Fairness: ETF: Safety and robustness","Data Mining: DM: Mining graphs","Machine Learning: ML: Sequence and graph learning","Machine Learning: ML: Robustness"],"videos":[],"references":[{"position":1,"raw":"[Aïmeur et al., 2023] Esma Aïmeur, Sabrine Amri, and Gilles Brassard. 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