Hugging Face Trending Papers
Decoupling Consistency and Conflict across MLLM Layers for Fake News Detection
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arXiv Computation and Language
5d ago
Decoupling Consistency and Conflict across MLLM Layers for Fake News Detection
The paper introduces Multi-Level Consistency and Conflict Decoupling (MLCCD), a method for detecting fake news in multimodal content by leveraging the layered knowledge of multimodal large language models (MLLMs). MLCCD first selects relevant visual and textual tokens using the MLLM’s final decision representation, then identifies tokens whose representations change sharply across adjacent layers to capture layer‑specific knowledge. Finally, it decouples this knowledge into consistency and conflict evidence, aggregating each across layers with dedicated routers that adapt to varying conflict levels, achieving superior performance on three benchmarks.
arXiv AI
Jun 3
CORE: Conflict-Oriented Reasoning for General Multimodal Manipulation Detection
arXiv:2606. 03066v1 Announce Type: new Abstract: The rapid rise of generative AI has made multimodal fake news increasingly realistic and pervasive, posing severe threats to public trust and social stability.
arXiv AI
Sep 30
Can Multimodal Large Language Models Generate and Detect Multimodal Social Media Fake News?
The paper investigates whether multimodal large language models (MLLMs) can generate and detect realistic multimodal fake news on social media. Using a multi‑agent framework—comprising a story agent, an image agent, and a critic agent—the authors produced over 9,000 paired multimodal news posts across science, health, and entertainment domains. They benchmarked 16 open‑ and closed‑source MLLMs for automated detection and found that most models fall far short of human accuracy, especially in identifying image authenticity, highlighting the need for stronger defenses against social media fake news.
arXiv Computation and Language
Sep 30
Rethinking Multimodal Fake News Detection in the Generative AI Era
arXiv:2609.36850v1 Announce Type: new Abstract: Generative content is increasingly entering the production and dissemination of news, transforming fake news from manually fabricated or simply manipul...
arXiv Machine Learning
Jun 9
KITE: A Tri-Modal Transformer Integrating Text, Images, and Knowledge Graphs for Fake News Detection
arXiv:2606. 07651v1 Announce Type: new Abstract: Traditional fake news detection methods are falling behind as multimodal misinformation grows more advanced, seamlessly blending deceptive text, manipulated visuals, and factually incorrect claims.
arXiv Machine Learning
Jul 14
Multimodal rumor detection enhanced by external evidence and forgery features
arXiv:2601. 14954v3 Announce Type: replace Abstract: Social media increasingly disseminates information through mixed image text posts, but rumors often exploit subtle inconsistencies and forged content, making detection based solely on post content difficult.