多模式变压器用于假新闻检测.
Pingping Yang1, Jiachen Ma1, Yong Liu1
1Heilongjiang University, Harbin 150000, China.
Mathematical biosciences and engineering : MBE
|September 7, 2023
概括
这项研究介绍了TGA,这是一种基于变压器的新型模型,用于多式联运假新闻检测. 使用注意力机制,TGA有效地融合了图像和文本特征,大大提高了检测准确度.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 信息安全 信息安全
背景情况:
- 社交媒体上的假新闻构成了重大的社会威胁,特别是当它包含多媒体元素时.
- 现有的多模式假新闻检测方法难以提取高质量的视觉特征并有效地融合多模式信息.
- 当前的模型往往无法利用虚假新闻中图像和文本之间的特征差异.
研究的目的:
- 提出一种基于变压器的新型模型,TGA,用于增强多式联运假新闻检测.
- 在现有的检测方法中解决特征提取和融合方面的局限性.
- 提高虚假新闻检测系统的准确性和稳定性.
主要方法:
- 利用变压器模型来清晰地提取文本和图像特征.
- 实施了注意力机制,以有效地融合多式联运特征.
- 在分类器中将文本和图像之间的特征相似程度纳入分类器.
主要成果:
- 拟议的TGA模型在公共数据集上表现出显著的有效性.
- 实验结果验证了TGA在现有的多模式假新闻检测方法上的优势.
- 基于注意力的融合和特征相似性的利用有助于提高检测性能.
结论:
- TGA模型在多模式假新闻检测方面提供了有前途的进步.
- 基于变压器的特征提取和注意力融合是提高检测精度的关键.
- 利用特征相似性对于强大的假新闻识别至关重要.
相关概念视频
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