{
  "id": 4835,
  "url": "https://arxiv.org/abs/2605.06143v1",
  "title": "AI-Generated Images: What Humans and Machines See When They Look at the Same Image",
  "summary": "The misuse of generative AI in online disinformation campaigns highlights the urgent need for transparent and explainable detection systems. In this work, we investigate how detectors for AI-generated images can be more effective in providing human-understandable explanations for their predictions. To this end, we develop a suite of detectors with various architectures and fine-tuning strategies, trained on our large-scale photorealistic fake image dataset, AIText2Image, and assess their perform",
  "authors": "Silvia Poletti, Justin Ilyes, Marcel Hasenbalg, David Fischinger, Martin Boyer",
  "category": "research",
  "topics": "misinformation,transparency,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-07T12:40:13.000Z",
  "fetched_at": "2026-07-14T16:31:17.583Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4835",
  "original_url": "https://arxiv.org/abs/2605.06143v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}