How to Distinguish AI-Generated Images from Authentic Photographs
On the map: Diffusion models → Image generation, text-to-image and editing
Abstract (arXiv)
The high level of photorealism in state-of-the-art diffusion models like Midjourney, Stable Diffusion, and Firefly makes it difficult for untrained humans to distinguish between real photographs and AI-generated images. To address this problem, we designed a guide to help readers develop a more critical eye toward identifying artifacts, inconsistencies, and implausibilities that often appear in AI-generated images. The guide is organized into five categories of artifacts and implausibilities: anatomical, stylistic, functional, violations of physics, and sociocultural. For this guide, we generated 138 images with diffusion models, curated 9 images from social media, and curated 42 real photographs. These images showcase the kinds of cues that prompt suspicion towards the possibility an image is AI-generated and why it is often difficult to draw conclusions about an image's provenance without any context beyond the pixels in an image. Human-perceptible artifacts are not always present in AI-generated images, but this guide reveals artifacts and implausibilities that often emerge. By drawing attention to these kinds of artifacts and implausibilities, we aim to better equip people to distinguish AI-generated images from real photographs in the future.
Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models. First posted on arXiv 12 Jun 2024.
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Citing papers per year: 2025: 8; 2026: 4.
12 papers.
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- 2025AIGI-Holmes: Towards Explainable and Generalizable AI-Generated Image Detection via Multimodal Large Language Modelscited by 33
- 2025Characterizing Photorealism and Artifacts in Diffusion Model-Generated Imagescited by 14
- 2025A Crack in the Bark: Leveraging Public Knowledge to Remove Tree-Ring Watermarkscited by 4
- 2025TruthLens:A Training-Free Paradigm for DeepFake Detectioncited by 1
- 2025A Practical Synthesis of Detecting AI-Generated Textual, Visual, and Audio Contentcited by 1
- 2025Designing Effective Digital Literacy Interventions for Boosting Deepfake Discernmentcited by 1
- 2025A Study of Commonsense Reasoning over Visual Object Propertiescited by 1
- 2026Visual Persuasion: What Influences Decisions of Vision-Language Models?cited by 1
- 2026Generative AI Literacy Training Improves Intelligence Analysts' Discrimination of Real and AI-Generated Imagescited by 0
- 2026When Composition Doesn't Add Up: Humans Identifying Defects in AI-Generated Imagescited by 0
- 2026Calibrating LLM Explanations to Human Decisions with Reinforcement Learningcited by 0
- 2026Calibrating LLM Explanations to Human Decisions with Reinforcement Learning12 Oct 2026
- 2026When Composition Doesn't Add Up: Humans Identifying Defects in AI-Generated Images26 Aug 2026
- 2026Generative AI Literacy Training Improves Intelligence Analysts' Discrimination of Real and AI-Generated Images26 Jun 2026
- 2026Visual Persuasion: What Influences Decisions of Vision-Language Models?17 Feb 2026
- 2025A Study of Commonsense Reasoning over Visual Object Properties14 Aug 2025
- 2025Designing Effective Digital Literacy Interventions for Boosting Deepfake Discernment31 Jul 2025
- 2025AIGI-Holmes: Towards Explainable and Generalizable AI-Generated Image Detection via Multimodal Large Language Models3 Jul 2025
- 2025A Crack in the Bark: Leveraging Public Knowledge to Remove Tree-Ring Watermarks12 Jun 2025
- 2025A Practical Synthesis of Detecting AI-Generated Textual, Visual, and Audio Content2 Apr 2025
- 2025TruthLens:A Training-Free Paradigm for DeepFake Detection19 Mar 2025
- 2025Deepfake-Eval-2024: A Multi-Modal In-the-Wild Benchmark of Deepfakes Circulated in 20244 Mar 2025
- 2025Characterizing Photorealism and Artifacts in Diffusion Model-Generated Images17 Feb 2025
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