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基于图像和文本的假新闻检测与转移学习

Esther Irawati Setiawan¹, Patrick Sutanto¹, Christian Nathaniel Purwanto¹

¹Information Technology Department, Institut Sains dan Teknologi Terpadu Surabaya, Surabaya, Jawa Timur, Indonesia.

|June 17, 2025

概括

这项研究引入了一种多式联络方法,通过分析文本和图像来检测假新闻. 结合这些,以及高效的微调,实现了83%的准确性,提高了低数据场景的可靠性.

科学领域:

人工智能的人工智能
计算机科学计算机科学
信息科学信息科学信息科学

背景情况:

在数字时代,假新闻对信息可靠性构成重大威胁.
目前的假新闻检测方法往往忽视视觉内容,这对于识别错误信息至关重要.
有限的标记数据对训练有效的假新闻检测模型构成了重大挑战.

研究的目的:

提出一种多式联运分类方法,以加强假新闻的检测.
通过使用高效的微调技术,解决虚假新闻检测数据稀缺的挑战.
评估结合文字和视觉信息的有效性,以提高准确性.

主要方法:

利用CLIP (对比语言-图像预训练) 进行联合文本-图像特征提取.
采用LoRA (低级调整),一个参数高效的微调方法,以调整CLIP用于假新闻检测.
使用简单的单层多层感知子 (MLP) 来对多式联络特征进行分类.

主要成果:

通过使用多式联络方法与LoRA微调,在分类假新闻方面取得了83%的准确性.
证明了多式联络学习在提高假新闻检测性能方面的有效性.
展示了参数高效的微调技术在低资源环境中的好处.

结论:

多模式学习,整合文本和图像数据,显著提高了假新闻检测能力.
像LoRA这样的参数高效的微调技术对于开发具有有限数据的强大的假新闻检测器至关重要.
提出的方法为提高在线信息可靠性提供了一个有希望的解决方案.

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