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CLAAF:基于对比学习和自适应的Agg模式融合的多式假信息检测
Guangyu Mu1,2, Chuanzhi Chen1, Xiurong Li3
1School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun, China.
PloS one
|May 7, 2025
概括
这项研究介绍了对比学习和适应性攻击模式融合 (CLAAF) 模型,以打击多式联络虚假信息. 通过对齐文本和图像特征并适应地融合模式,CLAAF提高了检测准确度,改善了内容真实性验证.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 信息科学 信息科学 信息科学
背景情况:
- 社交媒体虚假信息给内容真实性验证带来了挑战,特别是在多式联络环境中.
- 简单的融合方法与不同的特征表示作斗争,导致噪声和检测精度降低.
研究的目的:
- 为改进多式联络虚假信息检测提出一种新型模型,即对比学习和适应性聚变模式融合 (CLAAF).
- 解决简单模式融合在处理噪声和提高精度方面的局限性.
主要方法:
- 开发了一种对比式学习策略,以调整文本和图像模式,保留关键功能并减少噪音.
- 引入了一种适应性聚合模块,用于深度的模式间交互和集成.
- 从权威的新闻和事实核查平台构建了一个全面的多式联运数据集.
主要成果:
- 与现有的基线模型相比,CLAAF模型的准确性得到了3.45%的改善.
- 在检测多式联运虚假信息方面实现了更高的精度和稳定性.
- 开发的数据集为模型培训和验证提供了坚实的基础.
结论:
- 该CLAAF模型有效地减轻噪音,并提高了多式联运虚假信息检测的准确性.
- 适应融合和对比学习是提高模型性能的关键.
- 这些发现有助于在数字时代更可靠地验证内容真实性.
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