mlsanityData to 7 Oct 2026

GenImage: A Million-Scale Benchmark for Detecting AI-Generated Image

arXiv 2306.08571 ↗First posted 14 Jun 2023cs.CVcited by 318

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Cited by 47 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

The extraordinary ability of generative models to generate photographic images has intensified concerns about the spread of disinformation, thereby leading to the demand for detectors capable of distinguishing between AI-generated fake images and real images. However, the lack of large datasets containing images from the most advanced image generators poses an obstacle to the development of such detectors. In this paper, we introduce the GenImage dataset, which has the following advantages: 1) Plenty of Images, including over one million pairs of AI-generated fake images and collected real images. 2) Rich Image Content, encompassing a broad range of image classes. 3) State-of-the-art Generators, synthesizing images with advanced diffusion models and GANs. The aforementioned advantages allow the detectors trained on GenImage to undergo a thorough evaluation and demonstrate strong applicability to diverse images. We conduct a comprehensive analysis of the dataset and propose two tasks for evaluating the detection method in resembling real-world scenarios. The cross-generator image classification task measures the performance of a detector trained on one generator when tested on the others. The degraded image classification task assesses the capability of the detectors in handling degraded images such as low-resolution, blurred, and compressed images. With the GenImage dataset, researchers can effectively expedite the development and evaluation of superior AI-generated image detectors in comparison to prevailing methodologies.

Key facts. Cited by 318 papers in the map (data through 7 Oct 2026). Builds on 28 of its 38 references that are in the map. First posted on arXiv 14 Jun 2023.

Builds on

28 of 38 references.

References · 28

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  3. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  4. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  5. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  6. 2020Training data-efficient image transformers & distillation through attentioncited by 4,560
  7. 2022LAION-5B: An open large-scale dataset for training next generation image-text modelscited by 4,131
  8. 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Modelscited by 3,595
  9. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,967
  10. 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,609
  11. 2016Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoisingcited by 1,352
  12. 2019CNN-generated images are surprisingly easy to spot... for nowcited by 842
16 more
  1. 2021Vector Quantized Diffusion Model for Text-to-Image Synthesiscited by 773
  2. 2019Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensicscited by 573
  3. 2020Thinking in Frequency: Face Forgery Detection by Mining Frequency-aware Cluescited by 518
  4. 2023DIRE for Diffusion-Generated Image Detectioncited by 386
  5. 2022DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Modelscited by 376
  6. 2018Exposing Deep Fakes Using Inconsistent Head Posescited by 294
  7. 2019Detecting and Simulating Artifacts in GAN Fake Imagescited by 293
  8. 2020Watch your Up-Convolution: CNN Based Generative Deep Neural Networks are Failing to Reproduce Spectral Distributionscited by 278
  9. 2019On the Detection of Digital Face Manipulationcited by 259
  10. 2020Global Texture Enhancement for Fake Face Detection in the Wildcited by 220
  11. 2018DeepFakes: a New Threat to Face Recognition? Assessment and Detectioncited by 208
  12. 2021ForgeryNet: A Versatile Benchmark for Comprehensive Forgery Analysiscited by 124
  13. 2022Towards the Detection of Diffusion Model Deepfakescited by 111
  14. 2023CIFAKE: Image Classification and Explainable Identification of AI-Generated Synthetic Imagescited by 84
  15. 2020Adversarial Perturbations Fool Deepfake Detectorscited by 48
  16. 2023Benchmarking Deepart Detectioncited by 12

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 7; 2024: 65; 2025: 111; 2026: 135.

318 papers.

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  1. 2024A Sanity Check for AI-generated Image Detectioncited by 187
  2. 2023DeepfakeBench: A Comprehensive Benchmark of Deepfake Detectioncited by 162
  3. 2024DF40: Toward Next-Generation Deepfake Detectioncited by 126
  4. 2023PatchCraft: Exploring Texture Patch for Efficient AI-generated Image Detectioncited by 119
  5. 2024C2P-CLIP: Injecting Category Common Prompt in CLIP to Enhance Generalization in Deepfake Detectioncited by 111
  6. 2024SIDA: Social Media Image Deepfake Detection, Localization and Explanation with Large Multimodal Modelcited by 107
  7. 2024Orthogonal Subspace Decomposition for Generalizable AI-Generated Image Detectioncited by 102
  8. 2024Improving Synthetic Image Detection Towards Generalization: An Image Transformation Perspectivecited by 88
  9. 2024DeMamba: AI-Generated Video Detection on Million-Scale GenVideo Benchmarkcited by 85
  10. 2024Detecting Multimedia Generated by Large AI Models: A Surveycited by 73
  11. 2024A Bias-Free Training Paradigm for More General AI-generated Image Detectioncited by 67
  12. 2025Spot the Fake: Large Multimodal Model-Based Synthetic Image Detection with Artifact Explanationcited by 66
38 more
  1. 2025LEGION: Learning to Ground and Explain for Synthetic Image Detectioncited by 65
  2. 2025Dual Data Alignment Makes AI-Generated Image Detector Easier Generalizablecited by 58
  3. 2025GPT-ImgEval: A Comprehensive Benchmark for Diagnosing GPT4o in Image Generationcited by 57
  4. 2024Community Forensics: Using Thousands of Generators to Train Fake Image Detectorscited by 50
  5. 2024Real-Time Deepfake Detection in the Real-Worldcited by 49
  6. 2025Is Artificial Intelligence Generated Image Detection a Solved Problem?cited by 49
  7. 2024LaRE2: Latent Reconstruction Error Based Method for Diffusion-Generated Image Detectioncited by 47
  8. 2024A Single Simple Patch is All You Need for AI-generated Image Detectioncited by 43
  9. 2024Fake or JPEG? Revealing Common Biases in Generated Image Detection Datasetscited by 43
  10. 2024D3: Scaling Up Deepfake Detection by Learning from Discrepancycited by 43
  11. 2025CO-SPY: Combining Semantic and Pixel Features to Detect Synthetic Images by AIcited by 42
  12. 2024Diffusion Facial Forgery Detectioncited by 41
  13. 2023Robustness and Generalizability of Deepfake Detection: A Study with Diffusion Modelscited by 36
  14. 2023GenDet: Towards Good Generalizations for AI-Generated Image Detectioncited by 36
  15. 2024FakeBench: Probing Explainable Fake Image Detection via Large Multimodal Modelscited by 36
  16. 2024RIGID: A Training-free and Model-Agnostic Framework for Robust AI-Generated Image Detectioncited by 36
  17. 2025Ivy-Fake: A Unified Explainable Framework and Benchmark for Image and Video AIGC Detectioncited by 34
  18. 2025AIGI-Holmes: Towards Explainable and Generalizable AI-Generated Image Detection via Multimodal Large Language Modelscited by 33
  19. 2024WildFake: A Large-scale Challenging Dataset for AI-Generated Images Detectioncited by 31
  20. 2024MISC: Ultra-low Bitrate Image Semantic Compression Driven by Large Multimodal Modelcited by 29
  21. 2024DiffusionFace: Towards a Comprehensive Dataset for Diffusion-Based Face Forgery Analysiscited by 29
  22. 2025BusterX++: Towards Unified Cross-Modal AI-Generated Content Detection and Explanation with MLLMcited by 28
  23. 2025Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarioscited by 24
  24. 2025GenVidBench: A 6-Million Benchmark for AI-Generated Video Detectioncited by 23
  25. 2025All Patches Matter, More Patches Better: Enhance AI-Generated Image Detection via Panoptic Patch Learningcited by 23
  26. 2023Exploring the Naturalness of AI-Generated Imagescited by 22
  27. 2025ForensicHub: A Unified Benchmark & Codebase for All-Domain Fake Image Detection and Localizationcited by 22
  28. 2023Rethinking Image Editing Detection in the Era of Generative AI Revolutioncited by 21
  29. 2024Organic or Diffused: Can We Distinguish Human Art from AI-generated Images?cited by 21
  30. 2024Mixture of Low-rank Experts for Transferable AI-Generated Image Detectioncited by 21
  31. 2025Toward Generalizable Forgery Detection and Reasoningcited by 20
  32. 2024GIM: A Million-scale Benchmark for Generative Image Manipulation Detection and Localizationcited by 19
  33. 2026NTIRE 2026 Challenge on Robust AI-Generated Image Detection in the Wildcited by 18
  34. 2024Semi-Truths: A Large-Scale Dataset of AI-Augmented Images for Evaluating Robustness of AI-Generated Image detectorscited by 17
  35. 2025Zooming In on Fakes: A Novel Dataset for Localized AI-Generated Image Detection with Forgery Amplification Approachcited by 16
  36. 2025Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detectioncited by 16
  37. 2025DenseWorld-1M: Towards Detailed Dense Grounded Caption in the Real Worldcited by 16
  38. 2024MiRAGeNews: Multimodal Realistic AI-Generated News Detectioncited by 15

50 most cited of 318 All 318 (JSON)

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  1. 2023Towards Universal Fake Image Detectors that Generalize Across Generative Models
  2. 2024Synthetic Photography Detection: A Visual Guidance for Identifying Synthetic Images Created by AI
  3. 2023Improving Synthetically Generated Image Detection in Cross-Concept Settings
  4. 2023Intriguing properties of synthetic images: from generative adversarial networks to diffusion models
  5. 2026R2BD: A Reconstruction-Based Method for Generalizable and Efficient Detection of Fake Images
  6. 2022DE-FAKE: Detection and Attribution of Fake Images Generated by Text-to-Image Generation Models
  7. 2026A Comprehensive Dataset for Human vs. AI Generated Image Detection
  8. 2025Toward Generalized Detection of Synthetic Media: Limitations, Challenges, and the Path to Multimodal Solutions