基于立场分析的脱离上下文虚假信息检测方法
Out-of-context misinformation detection method based on stance analysis
袁鑫 1郭捷 1邱卫东 1黄征1
作者信息
- 1. 上海交通大学网络空间安全学院,上海 200240
- 折叠
摘要
随着基于人工智能的自动生成技术的发展,虚假信息以更具欺骗性的形式出现,对经济和社会秩序造成了巨大危害.脱离上下文虚假信息是最具欺骗性和最容易实现的虚假信息类型之一,恶意攻击者通过歪曲真实图像中的人物、事件、地点等上下文信息来增强虚假叙事的可信度.针对已有检测算法严重依赖知识库、未能全面考虑待检测信息与互联网证据之间的立场关系的问题,提出了一种基于立场分析的脱离上下文虚假信息检测方法.对于需要检测的图像-标题对以及在互联网上检索到的文本证据与视觉证据,该方法根据标题与文本证据中命名实体的共现关系,为每条文本证据计算一个立场增益分数;使用互相独立的立场分析网络对图像与视觉证据、标题与文本证据分别进行层次聚类,并完成基于多个注意力机制与立场分析模块的语义立场表征抽取;根据语义比较与立场分析的结果对图像-标题对的真实性进行预测.实验结果表明,得益于立场分析的引入,所提方法能够显著提高检测效果,相比其他使用互联网证据进行检测的最佳算法准确率提升了2.3%.
Abstract
As artificial intelligence-based automatic generation technology advances,the emergence of misinformation in more sophisticated guises has become a significant threat to the economy and social order.Among its forms,out-of-context misinformation stands out as particularly deceptive and readily executable.This type of misinformation involves malicious actors enhancing the credibility of false narratives by misrepresenting contextual details such as individuals,events,and locations within real images.To address the shortcomings of current detection algorithms,which heavily depend on knowledge bases and often overlook the stance relationship between the information under scrutiny and online evidence,a stance analysis-based out-of-context misinformation detection method was developed.This method involved several steps for the detection of an image-caption pair along with the corresponding textual and visual evidence retrieved from the Internet.Initially,a stance gain score for each piece of textual evidence was calculated based on the co-occurrence of named entities.Subsequently,independent stance analysis networks were utilized to perform hierarchical clustering on both the image and visual evidence,as well as on the caption and textual evidence.This process involved the extraction of semantic stance representations,facilitated by multiple attention mechanisms and a stance analysis module.The authenticity of the image-caption pair was subsequently predicted based on the outcomes of semantic comparison and stance analysis.Experimental results indicate that the incorporation of stance analysis significantly enhances the method's detection capabilities.Specifically,the accuracy of this method outperforms the state-of-the-art algorithm that employs Internet evidence for detection by 2.3%.
关键词
脱离上下文/立场分析/虚假信息/互联网证据Key words
out-of-context/stance analysis/misinformation/internet evidence引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据