CORE: Conflict-Oriented Reasoning for General Multimodal Manipulation Detection
📰 ArXiv cs.AI
Learn to detect multimodal manipulation using conflict-oriented reasoning with CORE, improving generalization to new manipulation types
Action Steps
- Apply conflict-oriented reasoning to detect semantic conflicts in multimodal data
- Use CORE to identify physical inconsistencies in manipulated media
- Train a model to recognize intrinsic conflicts in misinformation
- Evaluate the performance of CORE on a dataset with diverse manipulation types
- Compare the generalization capabilities of CORE with existing manipulation-specific models
Who Needs to Know This
AI researchers and engineers working on multimodal manipulation detection can benefit from this approach to improve their models' generalization capabilities
Key Insight
💡 Conflict-oriented reasoning can improve detection of multimodal manipulation by identifying intrinsic conflicts in misinformation
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🚨 Detect multimodal manipulation with CORE! 🚨
Key Takeaways
Learn to detect multimodal manipulation using conflict-oriented reasoning with CORE, improving generalization to new manipulation types
Full Article
Title: CORE: Conflict-Oriented Reasoning for General Multimodal Manipulation Detection
Abstract:
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. Existing detection methods rely heavily on manipulation-specific models and large-scale labeled data, resulting in poor generalization to emerging manipulation types. We observed that the essence of manipulated misinformation lies in its intrinsic conflicts, \textbf{i.e.,} semantic or physic
Abstract:
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. Existing detection methods rely heavily on manipulation-specific models and large-scale labeled data, resulting in poor generalization to emerging manipulation types. We observed that the essence of manipulated misinformation lies in its intrinsic conflicts, \textbf{i.e.,} semantic or physic
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