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

arXiv: https://arxiv.org/abs/2404.15744 · HTML: https://mlsanity.com/paper/2404.15744/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 37 references that are in the map.
- First posted on arXiv 24 Apr 2024.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [Adversarial Attacks on Neural Networks for Graph Data](https://mlsanity.com/paper/1805.07984/) (2018)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [FANG: Leveraging Social Context for Fake News Detection Using Graph Representation](https://mlsanity.com/paper/2008.07939/) (2020)
- [User Preference-aware Fake News Detection](https://mlsanity.com/paper/2104.12259/) (2021)
- [Hiding Individuals and Communities in a Social Network](https://mlsanity.com/paper/1608.00375/) (2016)
- [Single Node Injection Attack against Graph Neural Networks](https://mlsanity.com/paper/2108.13049/) (2021)
- [Adversarial Attack on Large Scale Graph](https://mlsanity.com/paper/2009.03488/) (2020)
- [Attacking Fake News Detectors via Manipulating News Social Engagement](https://mlsanity.com/paper/2302.07363/) (2023)
- [Towards Reasonable Budget Allocation in Untargeted Graph Structure Attacks via Gradient Debias](https://mlsanity.com/paper/2304.00010/) (2023)
- [GAMC: An Unsupervised Method for Fake News Detection using Graph Autoencoder with Masking](https://mlsanity.com/paper/2312.05739/) (2023)
- [DECOR: Degree-Corrected Social Graph Refinement for Fake News Detection](https://mlsanity.com/paper/2307.00077/) (2023)
- [GIN-SD: Source Detection in Graphs with Incomplete Nodes via Positional Encoding and Attentive Fusion](https://mlsanity.com/paper/2403.00014/) (2024)
- [Cluster Attack: Query-based Adversarial Attacks on Graphs with Graph-Dependent Priors](https://mlsanity.com/paper/2109.13069/) (2021)
- [Minimum Topology Attacks for Graph Neural Networks](https://mlsanity.com/paper/2403.02723/) (2024)
- [My Brother Helps Me: Node Injection Based Adversarial Attack on Social Bot Detection](https://mlsanity.com/paper/2310.07159/) (2023)
- [Mining User-aware Multi-relations for Fake News Detection in Large Scale Online Social Networks](https://mlsanity.com/paper/2212.10778/) (2022)

## Built on by (most cited first, 11 of 11)

- [Hypergraph Attacks via Injecting Homogeneous Nodes into Elite Hyperedges](https://mlsanity.com/paper/2412.18365/) (2024)
- [KEN: Knowledge Augmentation and Emotion Guidance Network for Multimodal Fake News Detection](https://mlsanity.com/paper/2507.09647/) (2025)
- [SEER: Semantic Enhancement and Emotional Reasoning Network for Multimodal Fake News Detection](https://mlsanity.com/paper/2507.13415/) (2025)
- [A Dynamic Knowledge Update-Driven Model with Large Language Models for Fake News Detection](https://mlsanity.com/paper/2509.11687/) (2025)
- [RoBCtrl: Attacking GNN-Based Social Bot Detectors via Reinforced Manipulation of Bots Control Interaction](https://mlsanity.com/paper/2510.16035/) (2025)
- [Single-Node Trigger Backdoor Attacks in Graph-Based Recommendation Systems](https://mlsanity.com/paper/2506.08401/) (2025)
- [CogniDir: Combating Cognitive Malicious Comments via Adaptive Distributional Learning for Robust Fake News Detection](https://mlsanity.com/paper/2510.09712/) (2025)
- [Imperceptible Adversarial Attacks on Point Clouds Guided by Point-to-Surface Field](https://mlsanity.com/paper/2412.19015/) (2024)
- [CRC-SGAD: Conformal Risk Control for Supervised Graph Anomaly Detection](https://mlsanity.com/paper/2504.02248/) (2025)
- [Adaptive Graph Convolution and Semantic-Guided Attention for Multimodal Risk Detection in Social Networks](https://mlsanity.com/paper/2509.16936/) (2025)
- [Large Language Models in Misinformation Ecosystems: Misuse, Defense, and Vulnerability](https://mlsanity.com/paper/2607.10402/) (2026)

## Abstract (arXiv)

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.