The rapid proliferation of social media platforms has led to a surge in multimodal fake news, where deceptive content often combines text and images to mislead audiences. Traditional unimodal detection methods struggle to address the complexity of such content, necessitating holistic multimodal approaches. While the latest advancements in Multimodal Large Language Models (MLLMs) offer new opportunities for enhancing detection performance by analyzing multi-dimensional features, including source credibility, cross-modal contradictions, emotional bias, and manipulative writing patterns, these methods suffer from a key flaw: a susceptibility to hallucinations or erroneous reasoning, which can lead to flawed conclusions and ultimately biased detection results. We propose the Multimodal Fake News Detection via Multi-perspective Rationale Generation and Verification (MMRGV) model to mitigate this challenge. Our method employs a cross-verification mechanism to screen and reconcile contradictions among different rationales, thereby preserving the LLM's analytical advantages while mitigating the impact of erroneous reasoning or hallucinations on the final detection. Subsequently, these optimized rationales are fused via an adaptive weighting strategy to output a robust final prediction. Extensive experiments on three benchmark datasets (Twitter, Weibo, and GossipCop) demonstrate the superiority of our method, achieving state-of-the-art accuracy of 0.9972, 0.9663, and 0.8772, respectively, and significantly outperforming existing baselines. These results validate the effectiveness of multi-perspective rationale generation and cross-verification in enhancing multimodal fake news detection, offering a resilient solution to combat misinformation in the era of generative AI.

Fake news significantly influences decision-making processes by misleading individuals, organizations, and even governments. Large language models (LLMs), as part of generative AI, can amplify this problem by generating highly convincing fake news at scale, posing a significant threat to online information integrity. Therefore, understanding the motivations and mechanisms behind fake news generated by LLMs is crucial for effective detection and governance. In this study, we develop the LLM-Fake Theory, a theoretical framework that integrates various social psychology theories to explain machine-generated deception. Guided by this framework, we design an innovative prompt engineering pipeline that automates fake news generation using LLMs, eliminating manual annotation needs. Utilizing this pipeline, we create a theoretically informed \underline{M}achin\underline{e}-\underline{g}ener\underline{a}ted \underline{Fake} news dataset, MegaFake, derived from FakeNewsNet. Through extensive experiments with MegaFake, we advance both theoretical understanding of human-machine deception mechanisms and practical approaches to fake news detection in the LLM era.