范式转变：大型视觉语言模型在多模态虚假新闻检测中的全面调查
The Paradigm Shift: A Comprehensive Survey on Large Vision Language Models for Multimodal Fake News Detection
AI总结：
本文通过大型视觉语言模型全面调查多模态虚假新闻检测，分析其发展历程、技术挑战及未来方向。
摘要：
近年来，大型视觉语言模型（LVLMs）的快速演变推动了多模态虚假新闻检测（MFND）的范式转变，使其从传统的特征工程方法转变为统一的端到端多模态推理框架。早期方法主要依赖于浅层融合技术来捕捉文本和图像之间的相关性，但它们在高层语义理解和复杂跨模态交互方面存在困难。LVLMs的出现从根本上改变了这一局面，通过强大的表示学习实现了视觉和语言的联合建模，从而增强了检测利用文本叙述和视觉内容的虚假信息的能力。尽管取得了这些进展，该领域仍然缺乏一个系统性的调查来追踪这一转变并汇总最近的发展。为了解决这一缺口，本文通过LVLMs的视角对MFND进行了全面的回顾。我们首先提供了一种历史视角，将演变从传统的多模态检测流程映射到基础模型驱动的范式。接下来，我们建立了涵盖模型架构、数据集和性能基准的结构化分类法。此外，我们分析了剩余的技术挑战，包括可解释性、时间推理和领域泛化。最后，我们概述了未来的研究方向，以指导这一范式转变的下一阶段。据我们所知，这是第一个系统性地记录和分析LVLMs在对抗多模态虚假新闻中的变革作用的全面调查。现有方法的总结在我们的GitHub上：https://github.com/Tan-YiLong/Overview-of-Fake-News-Detection
英文摘要：
In recent years, the rapid evolution of large vision-language models (LVLMs) has driven a paradigm shift in multimodal fake news detection (MFND), transforming it from traditional feature-engineering approaches to unified, end-to-end multimodal reasoning frameworks. Early methods primarily relied on shallow fusion techniques to capture correlations between text and images, but they struggled with high-level semantic understanding and complex cross-modal interactions. The emergence of LVLMs has fundamentally changed this landscape by enabling joint modeling of vision and language with powerful representation learning, thereby enhancing the ability to detect misinformation that leverages both textual narratives and visual content. Despite these advances, the field lacks a systematic survey that traces this transition and consolidates recent developments. To address this gap, this paper provides a comprehensive review of MFND through the lens of LVLMs. We first present a historical perspective, mapping the evolution from conventional multimodal detection pipelines to foundation model-driven paradigms. Next, we establish a structured taxonomy covering model architectures, datasets, and performance benchmarks. Furthermore, we analyze the remaining technical challenges, including interpretability, temporal reasoning, and domain generalization. Finally, we outline future research directions to guide the next stage of this paradigm shift. To the best of our knowledge, this is the first comprehensive survey to systematically document and analyze the transformative role of LVLMs in combating multimodal fake news. The summary of existing methods mentioned is in our Github: \href{https://github.com/Tan-YiLong/Overview-of-Fake-News-Detection}{https://github.com/Tan-YiLong/Overview-of-Fake-News-Detection}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
