融合图像篡改检测与外部证据，提升虚假信息识别精度。
D-SECURE: Dual-Source Evidence Combination for Unified Reasoning in Misinformation Detection
- 双源证据融合：内部篡改检测+外部事实核查
- 在DGM4和ClaimReview上准确率显著提升
- 适合需要可解释性报告的虚假信息审核场景
多模态虚假信息日益将逼真的图像编辑与流畅但误导的文本结合，生成难以验证的说服性内容。现有系统通常依赖单一证据源：基于内容的检测器可发现图像与标题中的局部不一致，但无法判断全局事实真伪；基于检索的事实核查器则将输入视为粗粒度声明，常忽略细微的视觉或文本篡改。这种分离导致部分内部自洽的伪造内容绕过篡改检测，而核查器却验证了存在像素级或词元级篡改的声明。本文提出D-SECURE框架，融合内部篡改检测与外部证据推理，用于新闻类帖子的统一推理。该框架整合HAMMER篡改检测器与DEFAME检索管道：DEFAME执行广泛验证，而HAMMER分析残差或不确定案例中可能存在的细粒度编辑。在DGM4与ClaimReview数据集上的实验表明两者具有互补优势，支持其融合。最终生成包含篡改线索与外部证据的统一、可解释报告。
原文摘要 · Abstract (English)
Multimodal misinformation increasingly mixes realistic im-age edits with fluent but misleading text, producing persuasive posts that are difficult to verify. Existing systems usually rely on a single evidence source. Content-based detectors identify local inconsistencies within an image and its caption but cannot determine global factual truth. Retrieval-based fact-checkers reason over external evidence but treat inputs as coarse claims and often miss subtle visual or textual manipulations. This separation creates failure cases where internally consistent fabrications bypass manipulation detectors and fact-checkers verify claims that contain pixel-level or token-level corruption. We present D-SECURE, a framework that combines internal manipulation detection with external evidence-based reasoning for news-style posts. D-SECURE integrates the HAMMER manipulation detector with the DEFAME retrieval pipeline. DEFAME performs broad verification, and HAMMER analyses residual or uncertain cases that may contain fine-grained edits. Experiments on DGM4 and ClaimReview samples highlight the complementary strengths of both systems and motivate their fusion. We provide a unified, explainable report that incorporates manipulation cues and external evidence.
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