title: MiRAGeNews: Multimodal Realistic AI-Generated News Detection
summary: 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.
paper_authors._id: 670d33df6f346132013d7214
paper_authors.name: Runsheng Huang
paper_authors._id: 670d33df6f346132013d7215
paper_authors.name: Liam Dugan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-14T15:30:29.739Z
paper_authors._id: 6509e1e01b3694179dee256e
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/sUbok2yloLElaebkps3lT.png
paper_authors.fullname: Liam Dugan
paper_authors.type: user
paper_authors.user: liamdugan
paper_authors._id: 670d33df6f346132013d7216
paper_authors.name: Yue Yang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-21T15:06:17.308Z
paper_authors._id: 62f6c68904e5e02f82b04690
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/62f6c68904e5e02f82b04690/kK2-PkeAGzAOLhkfajswf.jpeg
paper_authors.fullname: Yue Yang
paper_authors.type: user
paper_authors.user: yyupenn
paper_authors._id: 670d33df6f346132013d7217
paper_authors.name: Chris Callison-Burch
paper_title: MiRAGeNews: Multimodal Realistic AI-Generated News Detection
paper_summary: 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.
paper_ai_keywords: multi-modal LLMs MiRAGeNews Dataset AI-generated fake news content image-caption pairs state-of-the-art generators F-1 multi-modal detector MiRAGe