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

• URL: http://arxiv.org/abs/2608.08009v1
• Date: Sat, 08 Aug 2026 08:41:26 GMT
• Title: Evidence-Grounded Forensic Reasoning for Detecting and Grounding Multi-Modal Media Manipulation
• Abstract summary: Fake news increasingly relies on cross-modal image-text forgeries.<n>Existing methods produce black-box detection results without any decision rationale.<n>We propose a multi-modal manipulation detector based on an Evidence-Grounded Forensic Reasoning framework.
• Score: 23.254574194880234
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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