Title: E-FreeM2: Efficient Training-Free Multi-Scale and Cross-Modal News Verification via MLLMs Show Chinese title

Title: E-FreeM2：通过MLLMs实现高效训练-free 多尺度和跨模态新闻验证

Abstract: The rapid spread of misinformation in mobile and wireless networks presents critical security challenges. This study introduces a training-free, retrieval-based multimodal fact verification system that leverages pretrained vision-language models and large language models for credibility assessment. By dynamically retrieving and cross-referencing trusted data sources, our approach mitigates vulnerabilities of traditional training-based models, such as adversarial attacks and data poisoning. Additionally, its lightweight design enables seamless edge device integration without extensive on-device processing. Experiments on two fact-checking benchmarks achieve SOTA results, confirming its effectiveness in misinformation detection and its robustness against various attack vectors, highlighting its potential to enhance security in mobile and wireless communication environments. Show Chinese abstract

Abstract: 移动和无线网络中虚假信息的快速传播带来了关键的安全挑战。 本研究介绍了一种无需训练的、基于检索的多模态事实验证系统，该系统利用预训练的视觉-语言模型和大语言模型进行可信度评估。 通过动态检索和交叉核对可信数据源，我们的方法减轻了传统基于训练的模型的漏洞，例如对抗攻击和数据污染。 此外，其轻量级设计使得在不进行大量本地处理的情况下，能够无缝集成到边缘设备中。 在两个事实核查基准上的实验取得了最先进（SOTA）的结果，证实了其在虚假信息检测中的有效性以及对各种攻击向量的鲁棒性，突显了其在增强移动和无线通信环境安全性方面的潜力。