Fact-Checking at Scale: Multimodal AI for Authenticity and Context Verification in Online Media
Van-Hoang Phan, Tung-Duong Le-Duc, Long-Khanh Pham, Anh-Thu Le, Quynh-Huong Dinh-Nguyen, Dang-Quan Vo, Hoang-Quoc Nguyen-Son, Anh-Duy Tran, Dang Vu, Minh-Son Dao

TL;DR
This paper introduces a comprehensive multimodal AI system for verifying the authenticity and context of online multimedia content, addressing misinformation challenges with advanced analysis techniques and practical verification tools.
Contribution
It presents a unified verification pipeline combining visual forensics, textual analysis, and multimodal reasoning, specifically designed for multilingual and real-world multimedia verification.
Findings
Effective detection of out-of-context media using semantic similarity, temporal, and geolocation cues.
Demonstrated system robustness across diverse real-world scenarios.
Advances multimedia verification capabilities for fact-checking and misinformation detection.
Abstract
The proliferation of multimedia content on social media platforms has dramatically transformed how information is consumed and disseminated. While this shift enables real-time coverage of global events, it also facilitates the rapid spread of misinformation and disinformation, especially during crises such as wars, natural disasters, or elections. The rise of synthetic media and the reuse of authentic content in misleading contexts have intensified the need for robust multimedia verification tools. In this paper, we present a comprehensive system developed for the ACM Multimedia 2025 Grand Challenge on Multimedia Verification. Our system assesses the authenticity and contextual accuracy of multimedia content in multilingual settings and generates both expert-oriented verification reports and accessible summaries for the general public. We introduce a unified verification pipeline that…
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Taxonomy
TopicsDigital Media Forensic Detection · Misinformation and Its Impacts · Generative Adversarial Networks and Image Synthesis
