针对多模态假新闻中某模态信息过载问题，提出新检测框架提升准确率。
Exploring Modality Disruption in Multimodal Fake News Detection
- 设计双阶段特征选择机制，识别并抑制干扰性模态
- 在FakeSV和FVC-2018上分别提升3.45%和3.71%准确率
- 适合关注多模态内容安全与模型鲁棒性的研究者
社交媒体的快速发展导致文本、图像、音频和视频等多种形式的假新闻广泛传播。相比单模态检测，多模态检测能利用跨模态信息提升效果。然而在社交语境下，某些模态可能包含夸张或过度表达的内容，造成模态干扰。本文定义该现象为模态干扰，并通过实验探究其影响。为此，提出FND-MoE多模态假新闻检测框架，并设计两阶段特征选择机制以减轻干扰。在FakeSV和FVC-2018数据集上的大量实验表明，该方法显著优于现有先进模型，在两个数据集上分别实现3.45%和3.71%的准确率提升。
原文摘要 · Abstract (English)
The rapid growth of social media has led to the widespread dissemination of fake news across multiple content forms, including text, images, audio, and video. Compared to unimodal fake news detection, multimodal fake news detection benefits from the increased availability of information across multiple modalities. However, in the context of social media, certain modalities in multimodal fake news detection tasks may contain disruptive or over-expressive information. These elements often include exaggerated or embellished content. We define this phenomenon as modality disruption and explore its impact on detection models through experiments. To address the issue of modality disruption in a targeted manner, we propose a multimodal fake news detection framework, FND-MoE. Additionally, we design a two-pass feature selection mechanism to further mitigate the impact of modality disruption. Extensive experiments on the FakeSV and FVC-2018 datasets demonstrate that FND-MoE significantly outperforms state-of-the-art methods, with accuracy improvements of 3.45% and 3.71% on the respective datasets compared to baseline models.
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