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MiRAGeNews: Multimodal Realistic AI-Generated News Detection

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arxiv 2410.09045 v1 pith:77HJ3P5O submitted 2024-10-11 cs.CV cs.CL

MiRAGeNews: Multimodal Realistic AI-Generated News Detection

Runsheng Huang , Liam Dugan , Yue Yang , Chris Callison-Burch This is my paper
classification cs.CV cs.CL
keywords ai-generatednewsdatasetcontentfakestate-of-the-artbecomegenerators
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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The proliferation of inflammatory or misleading "fake" news content has become increasingly common in recent years. Simultaneously, it has become easier than ever to use AI tools to generate photorealistic images depicting any scene imaginable. Combining these two -- AI-generated fake news content -- is particularly potent and dangerous. To combat the spread of AI-generated fake news, we propose the MiRAGeNews Dataset, a dataset of 12,500 high-quality real and AI-generated image-caption pairs from state-of-the-art generators. We find that our dataset poses a significant challenge to humans (60% F-1) and state-of-the-art multi-modal LLMs (< 24% F-1). Using our dataset we train a multi-modal detector (MiRAGe) that improves by +5.1% F-1 over state-of-the-art baselines on image-caption pairs from out-of-domain image generators and news publishers. We release our code and data to aid future work on detecting AI-generated content.

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Cited by 2 Pith papers

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