Global-Local Ensemble Detector for AI-Generated Fake News

IF 3.6 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS IEEE Access Pub Date : 2025-04-18 DOI:10.1109/ACCESS.2025.3562154

Yujia Wang;Wen Long

引用次数: 0

Abstract

With the continuous evolution of advanced large language models like GPT, the proliferation of AI-generated fake news presents growing challenges to information dissemination. Traditional text classification methods face difficulties in accurately detecting such content, due to their limited capacity to differentiate between authentic and fabricated news. To address this issue, this paper introduces a novel “Global-Local News Detection Model”, which combines BERT, Bidirectional Long Short-Term Memory (BiLSTM) networks, Text Convolutional Neural Networks (TextCNN), and attention mechanisms to enhance the detection of AI-generated fake news. A new dataset, generated using GPT-4 and covering 42 news categories, was developed to serve as a comprehensive and diverse foundation for training and evaluating the model. Experimental results indicate that the proposed model achieves an accuracy and F1 score of 0.82, surpassing traditional approaches.

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人工智能生成假新闻的全局-局部集成检测器

随着GPT等先进大型语言模型的不断发展，人工智能假新闻的泛滥给信息传播带来了越来越大的挑战。传统的文本分类方法在准确检测此类内容时面临困难，因为它们区分真实新闻和虚构新闻的能力有限。为了解决这一问题，本文引入了一种新的“全局-局部新闻检测模型”，该模型结合了BERT、双向长短期记忆（BiLSTM）网络、文本卷积神经网络（TextCNN）和注意机制来增强对人工智能生成的假新闻的检测。使用GPT-4生成的新数据集涵盖了42个新闻类别，为训练和评估模型提供了全面和多样化的基础。实验结果表明，该模型的准确率和F1分数均达到0.82，优于传统方法。

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来源期刊

IEEE Access COMPUTER SCIENCE, INFORMATION SYSTEMSENGIN-ENGINEERING, ELECTRICAL & ELECTRONIC

CiteScore

9.80

自引率

7.70%

发文量

6673

审稿时长

6 weeks

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