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 physical inconsistencies either across modalities or with common world knowledge. Inspired by this observa
The rapid advancement of generative AI has escalated the sophistication and prevalence of multimodal fake news, creating an urgent need for more robust detection methods.
This research addresses a critical vulnerability in information integrity, which is essential for maintaining public trust and social stability in an AI-saturated world.
The proposed CORE method allows for manipulation detection that is less reliant on specific models and large datasets, potentially improving generalization to novel manipulation types.
- · Information integrity platforms
- · Social media companies
- · Digital forensics
- · Fact-checking organizations
- · Malicious AI actors
- · Disinformation campaigns
- · Trust in digital media
Improved detection capabilities will help in identifying and mitigating the spread of generative AI-created fake news.
A more robust defense against misinformation could lead to the development of even more advanced adversarial AI techniques.
The arms race between AI generation and detection will continue to evolve, shaping future regulatory frameworks and digital citizenship.
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Read at arXiv cs.AI