MiRAGeNews: Multimodal Realistic AI-Generated News Detection
Authors: name: Runsheng Huang, name: Liam Dugan, name: Yue Yang, name: Chris Callison-Burch

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.