CORE: Conflict-Oriented Reasoning for General Multimodal Manipulation Detection
The article discusses a new framework called CORE, which stands for Conflict-Oriented Reasoning, designed to enhance the detection of multimodal manipulation in misinformation. CORE addresses the limitations of existing detection methods by focusing on intrinsic conflicts within manipulated content. The framework has shown promising results in adapting to new types of manipulation with minimal data requirements.
- ▪CORE aims to improve the detection of multimodal fake news by focusing on intrinsic conflicts in misinformation.
- ▪The framework utilizes a specially constructed Conflict Attribution Corpus for training conflict perception.
- ▪Experimental results indicate that CORE outperforms existing state-of-the-art models in manipulation detection.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2606.03066 |
| Publication time | Wed, 03 Jun 2026 00:00:00 -0400 |
| Retrieval time | 2026-06-03T04:11:55.408Z |
| Last seen | 2026-06-03T04:11:55.408Z |
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Computer Science > Artificial Intelligence arXiv:2606.03066 (cs) [Submitted on 2 Jun 2026] Title:CORE: Conflict-Oriented Reasoning for General Multimodal Manipulation Detection Authors:Jinjie Shen, Yaxiong Wang, Yujiao Wu, Lechao Cheng, Tianrui Hui, Nan Pu, Zhihui Li, Zhun Zhong View a PDF of the paper titled CORE: Conflict-Oriented Reasoning for General Multimodal Manipulation Detection, by Jinjie Shen and 7 other authors View PDF HTML (experimental) 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. Existing detection methods rely heavily on manipulation-specific models and large-scale labeled data, resulting in poor generalization to emerging manipulation types.
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