How good are humans at detecting AI-generated images? Learnings from an experiment
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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
Built on by
Citing papers per year: 2025: 2; 2026: 5.
7 papers.
- 2026Gram-MMD: A Texture-Aware Metric for Image Realism Assessmentcited by 1
- 2025The Unwinnable Arms Race of AI Image Detectioncited by 0
- 2025We are not able to identify AI-generated imagescited by 0
- 2026From AI-Generated Content to Agentic Action: Security and Safety Threats in Generative AIcited by 0
- 2026The CIFAR Synthetic Evidence Corpus for Detecting AI-Generated Evidencecited by 0
- 2026Generative AI Literacy Training Improves Intelligence Analysts' Discrimination of Real and AI-Generated Imagescited by 0
- 2026A Benchmark & Dataset for Detecting AI-Manipulated Visual Evidence in the Court Systemcited by 0
- 2026A Benchmark & Dataset for Detecting AI-Manipulated Visual Evidence in the Court System29 Sep 2026
- 2026Generative AI Literacy Training Improves Intelligence Analysts' Discrimination of Real and AI-Generated Images26 Jun 2026
- 2026The CIFAR Synthetic Evidence Corpus for Detecting AI-Generated Evidence6 Jun 2026
- 2026From AI-Generated Content to Agentic Action: Security and Safety Threats in Generative AI15 May 2026
- 2026Gram-MMD: A Texture-Aware Metric for Image Realism Assessment3 Apr 2026
- 2025We are not able to identify AI-generated images23 Dec 2025
- 2025The Unwinnable Arms Race of AI Image Detection25 Sep 2025
Similar, not cited
Close in wording, no citation either way
- 2024Organic or Diffused: Can We Distinguish Human Art from AI-generated Images?
- 2024How to Distinguish AI-Generated Images from Authentic Photographs
- 2025Could AI Trace and Explain the Origins of AI-Generated Images and Text?
- 2025Characterizing Photorealism and Artifacts in Diffusion Model-Generated Images
- 2024Visual Verity in AI-Generated Imagery: Computational Metrics and Human-Centric Analysis
- 2026Can You Trust What You See? Human and AI Detection of Synthetic Legal Evidence
- 2024AI-generated Image Detection: Passive or Watermark?
- 2025VERITAS: Verification and Explanation of Realness in Images for Transparency in AI Systems