mlsanityData to 7 Oct 2026

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

arXiv 2602.14441 ↗First posted 16 Feb 2026cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 22 of its 40 references that are in the map. First posted on arXiv 16 Feb 2026.

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22 of 40 references.

References · 22

  1. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  2. 2023Visual Instruction Tuningcited by 7,792
  3. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,582
  4. 2022Towards Reasoning in Large Language Models: A Surveycited by 593
  5. 2023GeoCLIP: Clip-Inspired Alignment between Locations and Images for Effective Worldwide Geo-localizationcited by 185
  6. 2021NewsCLIPpings: Automatic Generation of Out-of-Context Multimodal Mediacited by 106
  7. 2022End-to-End Multimodal Fact-Checking and Explanation Generation: A Challenging Dataset and Modelscited by 105
  8. 2023Detecting and Grounding Multi-Modal Media Manipulationcited by 98
  9. 2023Combating Misinformation in the Age of LLMs: Opportunities and Challengescited by 94
  10. 2023Detecting and Grounding Multi-Modal Media Manipulation and Beyondcited by 55
  11. 2019r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detectioncited by 53
  12. 2023Multimodal Automated Fact-Checking: A Surveycited by 51
10 more
  1. 2023Towards Reliable Misinformation Mitigation: Generalization, Uncertainty, and GPT-4cited by 45
  2. 2024DEFAME: Dynamic Evidence-based FAct-checking with Multimodal Expertscited by 42
  3. 2023Disinformation Detection: An Evolving Challenge in the Age of LLMscited by 41
  4. 2023VERITE: A Robust Benchmark for Multimodal Misinformation Detection Accounting for Unimodal Biascited by 37
  5. 2024RAGAR, Your Falsehood Radar: RAG-Augmented Reasoning for Political Fact-Checking using Multimodal Large Language Modelscited by 27
  6. 2021COSMOS: Catching Out-of-Context Misinformation with Self-Supervised Learningcited by 26
  7. 2024Multi-source Knowledge Enhanced Graph Attention Networks for Multimodal Fact Verificationcited by 5
  8. 2025Multimodal Fake News Detection: MFND Dataset and Shallow-Deep Multitask Learningcited by 5
  9. 2025From Prediction to Explanation: Multimodal, Explainable, and Interactive Deepfake Detection Framework for Non-Expert Userscited by 2
  10. 2025DGM4+: Dataset Extension for Global Scene Inconsistencycited by 2

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