MiRAGeNews: Multimodal Realistic AI-Generated News Detection

• URL: http://arxiv.org/abs/2410.09045v1
• Date: Fri, 11 Oct 2024 17:58:02 GMT
• Title: MiRAGeNews: Multimodal Realistic AI-Generated News Detection
• Authors: Runsheng Huang, Liam Dugan, Yue Yang, Chris Callison-Burch,
• Abstract summary: We propose the MiRAGeNews dataset to combat the spread of AI-generated fake news. Our dataset poses a significant challenge to humans. We train a multi-modal detector that improves by +5.1% F-1 over state-of-the-art baselines.
• Score: 45.067211436589126
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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