A General Black-box Adversarial Attack on Graph-based Fake News Detectors
Authors: Peican Zhu, Zechen Pan, Yang Liu, Jiwei Tian, Keke Tang, Zhen Wang
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on empirical datasets demonstrate the effectiveness of GAFSI. |
| Researcher Affiliation | Academia | 1School of Artificial Intelligence, Optics and Electronics, Northwestern Polytechnical University 2School of Computer Science, Northwestern Polytechnical University 3Air Traffic Control and Navigation College, Air Force Engineering University 4Cyberspace Institute of Advanced Technology, Guangzhou University |
| Pseudocode | No | The paper describes its method in prose within the 'Method' section but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about making its source code publicly available or provide links to a code repository. |
| Open Datasets | Yes | We adopt two real-world datasets [Shu et al., 2017; Fey and Lenssen, 2019], i.e., Politifact and Gossipcop, from the Py Torch-Geometric library. |
| Dataset Splits | Yes | To train detectors and our surrogate model, we split the data into 20% for the training, 10% for the validation, and 70% for the testing. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU models, CPU models, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Py Torch-Geometric library', 'Glove', and 'BERT' but does not specify version numbers for these software components or any other libraries. |
| Experiment Setup | No | The paper describes the general experimental settings, including dataset splits and types of GNN models used. However, it does not provide specific hyperparameters like learning rates, batch sizes, or optimizer settings for reproducibility. |