MiRAGeNews: Multimodal Realistic AI-Generated News Detection
Runsheng Huang, Liam D. Dugan, Yue Yang, Chris Callison-Burch · 2024
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-AIgenerated fake news content-is particularly potent and dangerous.To combat the spread of AI-generated fake news, we propose the Mi-RAGeNews Dataset, a dataset of 12,500 highquality 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 multimodal 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-ofdomain image generators and news publishers.We release our code and data to aid future work on detecting AI-generated content. 1