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

• URL: http://arxiv.org/abs/2602.14441v1
• Date: Mon, 16 Feb 2026 03:51:49 GMT
• Title: D-SECURE: Dual-Source Evidence Combination for Unified Reasoning in Misinformation Detection
• Authors: Gagandeep Singh, Samudi Amarasinghe, Priyanka Singh,
• Abstract summary: Multimodal misinformation increasingly mixes realistic im-age edits with fluent but misleading text, producing persuasive posts that are difficult to verify.<n>We present D-SECURE, a framework that combines internal manipulation detection with external evidence-based reasoning for news-style posts.
• Score: 9.049034101566642
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Multi-Sourced, Multi-Agent Evidence Retrieval for Fact-Checking [47.47518672198846]
  Misinformation spreading over the Internet poses a significant threat to both societies and individuals.<n>Previous methods rely on semantic and social-contextual patterns learned from training data.<n>We propose WKGFC, which exploits authorized open knowledge graph as a core resource of evidence.
  arXiv  Detail & Related papers  (2026-02-27T19:29:01Z)
• Contradiction to Consensus: Dual Perspective, Multi Source Retrieval Based Claim Verification with Source Level Disagreement using LLM [0.17188280334580197]
  Claim verification, a.k.a. fact-checking, can help identify potential misinformation.<n>Most automated claim verification systems depend on a single knowledge source.<n>We present a novel system for open-domain claim verification.
  arXiv  Detail & Related papers  (2026-02-21T02:21:31Z)
• What's Left Unsaid? Detecting and Correcting Misleading Omissions in Multimodal News Previews [31.373823254968315]
  Even when factually correct, social-media news previews can induce interpretation drift.<n>This covert harm is harder to detect than explicit misinformation yet remains underexplored.<n>We develop a multi-stage pipeline that disentangles and simulates preview-based versus context-based understanding.
  arXiv  Detail & Related papers  (2026-01-09T06:29:19Z)
• HiEAG: Evidence-Augmented Generation for Out-of-Context Misinformation Detection [8.609016163081744]
  We propose HiEAG, a novel Hierarchical Evidence-Augmented Generation framework to refine external consistency checking.<n>Our approach decomposes external consistency checking into a comprehensive engine pipeline, which integrates reranking and rewriting, apart from retrieval.<n>Our approach enables explanation for judgment, and achieves impressive performance with instruction tuning.
  arXiv  Detail & Related papers  (2025-11-18T01:11:48Z)
• Critical or Compliant? The Double-Edged Sword of Reasoning in Chain-of-Thought Explanations [60.27156500679296]
  We study the role of Chain-of-Thought (CoT) explanations in moral scenarios by systematically perturbing reasoning chains and manipulating delivery tones.<n>Our findings reveal two key effects: (1) users often trust with outcome agreement, sustaining reliance even when reasoning is flawed.<n>These results highlight how CoT explanations can simultaneously clarify and mislead, underscoring the need for NLP systems to provide explanations that encourage scrutiny and critical thinking rather than blind trust.
  arXiv  Detail & Related papers  (2025-11-15T02:38:49Z)
• Beyond Artificial Misalignment: Detecting and Grounding Semantic-Coordinated Multimodal Manipulations [56.816929931908824]
  We pioneer the detection of semantically-coordinated manipulations in multimodal data.<n>We propose a Retrieval-Augmented Manipulation Detection and Grounding (RamDG) framework.<n>Our framework significantly outperforms existing methods, achieving 2.06% higher detection accuracy on SAMM compared to state-of-the-art approaches.
  arXiv  Detail & Related papers  (2025-09-16T04:18:48Z)
• CRAVE: A Conflicting Reasoning Approach for Explainable Claim Verification Using LLMs [15.170312674645535]
  CRAVE is a Conflicting Reasoning Approach for explainable claim VErification.<n>It can verify complex claims based on the conflicting rationales reasoned by large language models.<n>CRAVE achieves much better performance than state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-04-21T07:20:31Z)
• Evidence Tampering and Chain of Custody in Layered Attestations [0.0]
  In distributed systems, trust decisions are made on the basis of integrity evidence generated via remote attestation. We present algorithms for identifying all such tampering opportunities for given evidence as well as tampering "strategies" by which an adversary can modify incriminating evidence without being detected. Our efforts are intended to help protocol designers ensure their protocols reduce evidence tampering opportunities to the smallest, most trustworthy set of components possible.
  arXiv  Detail & Related papers  (2024-01-31T21:54:53Z)
• FactCHD: Benchmarking Fact-Conflicting Hallucination Detection [64.4610684475899]
  FactCHD is a benchmark designed for the detection of fact-conflicting hallucinations from LLMs. FactCHD features a diverse dataset that spans various factuality patterns, including vanilla, multi-hop, comparison, and set operation. We introduce Truth-Triangulator that synthesizes reflective considerations by tool-enhanced ChatGPT and LoRA-tuning based on Llama2.
  arXiv  Detail & Related papers  (2023-10-18T16:27:49Z)
• Unleashing Potential of Evidence in Knowledge-Intensive Dialogue Generation [37.29386687125705]
  We propose a framework to effectively incorporate Evidence in knowledge-Intensive Dialogue Generation (u-EIDG) Specifically, we introduce an automatic evidence generation framework that harnesses the power of Large Language Models (LLMs) to mine reliable evidence labels from unlabeled data. By utilizing these evidence labels, we train a reliable evidence indicator to effectively identify relevant evidence from retrieved passages.
  arXiv  Detail & Related papers  (2023-09-15T13:13:30Z)
• Give Me More Details: Improving Fact-Checking with Latent Retrieval [58.706972228039604]
  Evidence plays a crucial role in automated fact-checking. Existing fact-checking systems either assume the evidence sentences are given or use the search snippets returned by the search engine. We propose to incorporate full text from source documents as evidence and introduce two enriched datasets.
  arXiv  Detail & Related papers  (2023-05-25T15:01:19Z)
• Synthetic Disinformation Attacks on Automated Fact Verification Systems [53.011635547834025]
  We explore the sensitivity of automated fact-checkers to synthetic adversarial evidence in two simulated settings. We show that these systems suffer significant performance drops against these attacks. We discuss the growing threat of modern NLG systems as generators of disinformation.
  arXiv  Detail & Related papers  (2022-02-18T19:01:01Z)
• AmbiFC: Fact-Checking Ambiguous Claims with Evidence [57.7091560922174]
  We present AmbiFC, a fact-checking dataset with 10k claims derived from real-world information needs. We analyze disagreements arising from ambiguity when comparing claims against evidence in AmbiFC. We develop models for predicting veracity handling this ambiguity via soft labels.
  arXiv  Detail & Related papers  (2021-04-01T17:40:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.