The rise of micro-videos has reshaped how misinformation spreads, amplifying its speed, reach, and impact on public trust. Existing benchmarks typically focus on a single deception type, overlooking the diversity of real-world cases that involve multimodal manipulation, AI-generated content, cognitive bias, and out-of-context reuse. Meanwhile, most detection models lack fine-grained attribution, limiting interpretability and practical utility. To address these gaps, we introduce WildFakeBench, a large-scale benchmark of over 10,000 real-world micro-videos covering diverse misinformation types and sources, each annotated with expert-defined attribution labels. Building on this foundation, we develop FakeAgent, a Delphi-inspired multi-agent reasoning framework that integrates multimodal understanding with external evidence for attribution-grounded analysis. FakeAgent jointly analyzes content and retrieved evidence to identify manipulation, recognize cognitive and AI-generated patterns, and detect out-of-context misinformation. Extensive experiments show that FakeAgent consistently outperforms existing MLLMs across all misinformation types, while WildFakeBench provides a realistic and challenging testbed for advancing explainable micro-video misinformation detection. Data and code are available at: https://github.com/Aiyistan/FakeAgent.

翻译：微视频的兴起重塑了虚假信息的传播方式，加速了其传播速度、扩大了覆盖范围，并加深了其对公众信任的影响。现有基准测试通常仅关注单一欺骗类型，忽视了涉及多模态操控、AI生成内容、认知偏差及脱离上下文复用等现实世界案例的多样性。与此同时，大多数检测模型缺乏细粒度归因能力，限制了可解释性和实际应用价值。为解决上述问题，我们提出了WildFakeBench——一个覆盖超过1万个真实世界微视频的大规模基准测试，涵盖多种虚假信息类型与来源，每个视频均附有专家定义的归因标签。在此基础之上，我们开发了FakeAgent——一个受德尔菲法启发的多智能体推理框架，该框架将多模态理解与外部证据相结合，实现基于归因的分析。FakeAgent联合分析内容与检索到的证据，以识别操控行为、识别认知与AI生成模式，并检测脱离上下文的虚假信息。大量实验表明，FakeAgent在所有虚假信息类型上均一致优于现有的多模态大语言模型（MMLMs），而WildFakeBench则为推动可解释的微视频虚假信息检测提供了真实且具挑战性的测试平台。数据和代码可在以下链接获取：https://github.com/Aiyistan/FakeAgent。