Multimedia Verification Through Multi-Agent Deep Research Multimodal Large Language Models

TL;DR

This paper introduces a multi-agent system utilizing multimodal large language models and specialized tools to verify multimedia content authenticity, extract contextual information, and trace sources in complex scenarios.

Contribution

It presents a novel multi-agent verification framework combining MLLMs with verification tools for comprehensive multimedia misinformation detection.

Findings

Successfully verified content authenticity in complex multimedia cases

Extracted precise geolocation and timing information

Traced source attribution across multiple platforms

Abstract

This paper presents our submission to the ACMMM25 - Grand Challenge on Multimedia Verification. We developed a multi-agent verification system that combines Multimodal Large Language Models (MLLMs) with specialized verification tools to detect multimedia misinformation. Our system operates through six stages: raw data processing, planning, information extraction, deep research, evidence collection, and report generation. The core Deep Researcher Agent employs four tools: reverse image search, metadata analysis, fact-checking databases, and verified news processing that extracts spatial, temporal, attribution, and motivational context. We demonstrate our approach on a challenge dataset sample involving complex multimedia content. Our system successfully verified content authenticity, extracted precise geolocation and timing information, and traced source attribution across multiple platforms, effectively addressing real-world multimedia verification scenarios.