mlsanityData to 14 Oct 2026

How good are humans at detecting AI-generated images? Learnings from an experiment

arXiv 2507.18640 ↗First posted 12 May 2025cs.HC cs.AI cs.CVcited by 7

Not on the map. Why?

Abstract (arXiv)

As AI-powered image generation improves, a key question is how well human beings can differentiate between "real" and AI-generated or modified images. Using data collected from the online game "Real or Not Quiz.", this study investigates how effectively people can distinguish AI-generated images from real ones. Participants viewed a randomized set of real and AI-generated images, aiming to identify their authenticity. Analysis of approximately 287,000 image evaluations by over 12,500 global participants revealed an overall success rate of only 62%, indicating a modest ability, slightly above chance. Participants were most accurate with human portraits but struggled significantly with natural and urban landscapes. These results highlight the inherent challenge humans face in distinguishing AI-generated visual content, particularly images without obvious artifacts or stylistic cues. This study stresses the need for transparency tools, such as watermarks and robust AI detection tools to mitigate the risks of misinformation arising from AI-generated content

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 3 of its 7 references that are in the map. First posted on arXiv 12 May 2025.

Builds on

3 of 7 references.

References · 3

  1. 2022Generative Adversarial Networkscited by 5,627
  2. 2023Using Human Feedback to Fine-tune Diffusion Models without Any Reward Modelcited by 252
  3. 2023Seeing is not always believing: Benchmarking Human and Model Perception of AI-Generated Imagescited by 66

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 5.

7 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2024Organic or Diffused: Can We Distinguish Human Art from AI-generated Images?
  2. 2024How to Distinguish AI-Generated Images from Authentic Photographs
  3. 2025Could AI Trace and Explain the Origins of AI-Generated Images and Text?
  4. 2025Characterizing Photorealism and Artifacts in Diffusion Model-Generated Images
  5. 2024Visual Verity in AI-Generated Imagery: Computational Metrics and Human-Centric Analysis
  6. 2026Can You Trust What You See? Human and AI Detection of Synthetic Legal Evidence
  7. 2024AI-generated Image Detection: Passive or Watermark?
  8. 2025VERITAS: Verification and Explanation of Realness in Images for Transparency in AI Systems