Robust Rumor Detection based on Multi-Defense Model Ensemble

Autor: Fan Yang, Shaomei Li
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Applied Artificial Intelligence, Vol 37, Iss 1 (2023)
Druh dokumentu: article
ISSN: 0883-9514
1087-6545
08839514
DOI: 10.1080/08839514.2022.2151174
Popis: The development of adversarial technology, represented by adversarial text, has brought new challenges to rumor detection based on deep learning. In order to improve the robustness of rumor detection models under adversarial conditions, we propose a robust detection method based on the ensemble of multi-defense model on the basis of several mainstream defense methods such as data enhancement, random smoothing, and adversarial training. First, multiple robust detection models are trained based on different defense principles; then, two different ensemble strategies are used to integrate the above models, and the detection effect under different ensemble strategies is studied. The test results on the open-source dataset Twitter15 show that the proposed method is able to compensate for the shortcomings of a single model by ensembling different decision boundaries to effectively defend against mainstream adversarial text attacks and improve the robustness of rumor detection models compared to existing defense methods.
Databáze: Directory of Open Access Journals
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