D-SECURE: Dual-Source Evidence Combination for Unified Reasoning in Misinformation Detection

2026-02-16

AI Summary

This work addresses the challenge that existing misinformation detection methods struggle to simultaneously handle realistic image manipulations and misleading textual claims, as reliance on a single evidence source cannot adequately support both localized tampering identification and global factual verification. To overcome this limitation, the paper proposes the first unified framework that jointly leverages internal tampering cues and external factual evidence. By coherently analyzing image-text consistency and integrating the HAMMER tampering detector with the DEFAME retrieval-based fact-checking pipeline, the approach enables complementary reasoning through fine-grained residual analysis and knowledge-grounded validation. Experiments on the DGM4 and ClaimReview datasets demonstrate that this dual-source method significantly improves detection of subtle image edits and semantic misinformation, while maintaining strong interpretability and robustness.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Application Domains: Misinformation & Fake News
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Responsible Web: Mitigating misinformation and disinformation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity

Abstract

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.

Problem

• misinformation detection
• multimodal misinformation
• manipulation detection
• fact-checking
• evidence combination

Innovation

• multimodal misinformation detection
• manipulation detection
• evidence-based reasoning
• dual-source fusion
• explainable verification