Clean-label Poisoning Attack against Fake News Detection Models

作者：Jiayi Liang, Xi Zhang, Yu-Ming Shang, Sanchuan Guo, Chaozhuo Li · 年份：2023 · DOI：10.1109/bigdata59044.2023.10386777 · 被引用次数：3 · 研究领域：Misinformation and Its Impacts、Spam and Phishing Detection、Topic Modeling

Researching data poisoning attacks against fake news detection models is crucial for bolstering their robustness and curbing the dissemination of fake news. Existing textual data poisoning attacks necessitate control over both the content and labels of news samples, making them impractical for real attack scenarios. In this paper, we propose COMCP, a novel clean-label poisoning attack model aimed at fake news detection models. Diverging from existing methods, COMCP ensures the poison samples are accurately labeled, while crafting stealthy poison comments without modifying the headlines or content, thereby enhancing the feasibility of the attack. Furthermore, COMCP generates poison comments by appending stealthy characters to ensure the stealthiness of the attack. Comprehensive experimental evaluations on three benchmark datasets illustrate that our proposal outperforms SOTA baselines in terms of attack success rate and text quality, while maintaining the accuracy of detecting clean samples.