Evidence-Grounded Forensic Reasoning for Detecting and Grounding Multi-Modal Media Manipulation

2026-08-08

AI Summary

Existing multimodal media forgery detection methods lack verifiable reasoning processes, leading to unreliable decisions and non-traceable evidence. To address this, this work proposes the Anchor-and-Verify forensic reasoning framework, which leverages modality-disentangled advantage routing to achieve modality-isolated perception and cross-modal comparison. The framework explicitly binds prediction outcomes to spatial evidence locations and incorporates a verifiable reward mechanism to optimize credit assignment in multitask training. This approach establishes, for the first time, a structured association between detection results and supporting evidence, achieving state-of-the-art performance in both forgery detection and localization while generating traceable and interpretable forensic reasoning records.

Abstract

Fake news increasingly relies on cross-modal image-text forgeries, making transparent and verifiable reasoning chains an urgent need for Detecting and Grounding Multi-Modal Media Manipulation (DGM4). Existing methods produce black-box detection results without any decision rationale, limiting their reliability in forensic practice. Multi-modal Large Language Models (MLLMs) offer a natural path toward explainability, but applying them to DGM4 raises two difficulties. First, models tend to generate explanations disconnected from predicted evidence locations, producing unverified attribution. Second, enforcing evidence-conclusion consistency requires active optimization, yet uniform training signals fail to distinguish localization tokens from classification tokens, making multi-head joint training unreliable. We propose a multi-modal manipulation detector based on an Evidence-Grounded Forensic Reasoning (EFR) framework. EFR introduces an Anchor-and-Verify reasoning chain that enforces modality-isolated perception before cross-modal comparison, with conclusion coordinates as explicit anchors to which downstream evidence must spatially correspond. A verifiable reward system then enforces evidence-conclusion consistency during training, while a Modality-Decoupled Advantage (MDA) routing mechanism mitigats credit misassignment across prediction tasks. Experiments show that EFR achieves state-of-the-art performance while producing structured forensic reasoning records that explicitly bind explanations to evidence.

Problem

• multi-modal media manipulation
• forensic reasoning
• evidence grounding
• explainability
• cross-modal forgery

Innovation

• Evidence-Grounded Reasoning
• Multi-Modal Manipulation Detection
• Anchor-and-Verify
• Modality-Decoupled Advantage
• Forensic Explainability