基于证据的取证推理：检测与定位多模态媒体篡改
Evidence-Grounded Forensic Reasoning for Detecting and Grounding Multi-Modal Media Manipulation
AI总结：
本文针对多模态媒体篡改检测与定位问题，提出基于证据的取证推理框架，结合锚定-验证推理链、可验证奖励系统与模态解耦优势路由机制，实现最优性能与可解释性的统一。
AI中文摘要：
虚假新闻越来越依赖跨模态图文伪造，使得透明且可验证的推理链成为检测与定位多模态媒体篡改（DGM4）的迫切需求。现有方法仅给出黑盒检测结果，无任何决策依据，限制了其在取证实践中的可靠性。多模态大语言模型（MLLM）为可解释性提供了自然途径，但将其应用于DGM4存在两大难点：一是模型倾向于生成与预测证据位置脱节的解释，产生无法验证的归因；二是强制证据与结论的一致性需要主动优化，而统一训练信号无法区分定位标记与分类标记，导致多头联合训练不可靠。本文提出一种基于证据的取证推理（EFR）框架的多模态篡改检测器。EFR引入锚定-验证推理链，在跨模态对比前执行模态隔离感知，将结论坐标作为明确锚点，下游证据必须在空间上与该锚点对应；可验证奖励系统在训练期间强制证据与结论的一致性，而模态解耦优势（MDA）路由机制缓解了预测任务间的信用分配错误。实验表明，EFR达到了当前最优性能，同时生成结构化的取证推理记录，将解释与证据明确绑定。
英文摘要：
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.
