CORE: Conflict-Oriented Reasoning for General Multimodal Manipulation Detection

2026-06-02 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

The CORE (Conflict-Oriented Reasoning) framework, published on 2026-06-02, introduces a novel approach for detecting multimodal manipulation, addressing the challenge of increasingly realistic generative AI-driven fake news. Unlike existing methods that rely on manipulation-specific models and extensive labeled data, CORE focuses on identifying intrinsic conflicts, such as semantic or physical inconsistencies across modalities or with common world knowledge. This framework enhances multimodal large language models (MLLMs) with explicit conflict-capturing capabilities. To facilitate this, CORE developed the Conflict Attribution Corpus (CAC), providing fine-grained annotations of conflict factors and sources. Through conflict-oriented representation enhancement and reasoning based on CAC, CORE demonstrates robust and generalizable detection, rapidly adapting to unseen manipulation types in few-shot or zero-shot settings. Extensive experiments confirm CORE's superior performance over state-of-the-art models, with its dataset and code publicly available.

Key takeaway

For machine learning engineers developing multimodal fake news detection systems, you should consider integrating conflict-oriented reasoning. Relying on manipulation-specific models limits generalization; instead, focus on identifying intrinsic semantic or physical inconsistencies across modalities or with world knowledge. This approach, exemplified by CORE, allows your systems to rapidly adapt to unseen manipulation types, even in few-shot or zero-shot scenarios, significantly improving robustness and reducing reliance on extensive labeled data for every new threat.

Key insights

Multimodal misinformation's essence lies in intrinsic conflicts, which CORE detects by enhancing MLLMs for generalizable manipulation detection.

Principles

• Intrinsic conflicts reveal multimodal misinformation.
• Conflict-oriented reasoning enables generalization.
• MLLMs benefit from explicit conflict-capturing.

Method

CORE constructs the Conflict Attribution Corpus (CAC) with fine-grained annotations. It then performs conflict-oriented representation enhancement and reasoning based on CAC to equip MLLMs with explicit conflict-capturing capabilities.

In practice

• Adapt to emerging manipulation types.
• Detect fake news with few samples.
• Perform zero-shot manipulation detection.

Topics

• Multimodal Manipulation Detection
• Conflict-Oriented Reasoning
• Multimodal Large Language Models
• Generative AI Misinformation
• Zero-shot Learning
• Conflict Attribution Corpus

Code references

• shen8424/CORE

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.