mlsanityData to 14 Oct 2026

Online Detection of AI-Generated Images

arXiv 2310.15150 ↗First posted 23 Oct 2023cs.CV cs.GR cs.LGcited by 55

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Abstract (arXiv)

With advancements in AI-generated images coming on a continuous basis, it is increasingly difficult to distinguish traditionally-sourced images (e.g., photos, artwork) from AI-generated ones. Previous detection methods study the generalization from a single generator to another in isolation. However, in reality, new generators are released on a streaming basis. We study generalization in this setting, training on N models and testing on the next (N+k), following the historical release dates of well-known generation methods. Furthermore, images increasingly consist of both real and generated components, for example through image inpainting. Thus, we extend this approach to pixel prediction, demonstrating strong performance using automatically-generated inpainted data. In addition, for settings where commercial models are not publicly available for automatic data generation, we evaluate if pixel detectors can be trained solely on whole synthetic images.

Key facts. Cited by 55 papers in the map (data through 14 Oct 2026). Builds on 44 of its 62 references that are in the map. First posted on arXiv 23 Oct 2023.

Builds on

44 of 62 references.

References · 44

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
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  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
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32 more
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  14. 2016Conditional Image Generation with PixelCNN Decoderscited by 862
  15. 2019CNN-generated images are surprisingly easy to spot... for nowcited by 847
  16. 2016Pixel Recurrent Neural Networkscited by 816
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  26. 2022DE-FAKE: Detection and Attribution of Fake Images Generated by Text-to-Image Generation Modelscited by 168
  27. 2018Learning Rich Features for Image Manipulation Detectioncited by 154
  28. 2018Fighting Fake News: Image Splice Detection via Learned Self-Consistencycited by 136
  29. 2019Detecting GAN generated Fake Images using Co-occurrence Matricescited by 124
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 21; 2025: 19; 2026: 12.

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  1. 2023Raising the Bar of AI-generated Image Detection with CLIPcited by 164
  2. 2024Detecting Multimedia Generated by Large AI Models: A Surveycited by 76
  3. 2023AntifakePrompt: Prompt-Tuned Vision-Language Models are Fake Image Detectorscited by 64
  4. 2023Shadows Don't Lie and Lines Can't Bend! Generative Models don't know Projective Geometry...for nowcited by 61
  5. 2024FakeInversion: Learning to Detect Images from Unseen Text-to-Image Models by Inverting Stable Diffusioncited by 61
  6. 2024Zero-Shot Detection of AI-Generated Imagescited by 50
  7. 2024Community Forensics: Using Thousands of Generators to Train Fake Image Detectorscited by 50
  8. 2024Fake or JPEG? Revealing Common Biases in Generated Image Detection Datasetscited by 43
  9. 2024Copyright Protection in Generative AI: A Technical Perspectivecited by 38
  10. 2024FakeBench: Probing Explainable Fake Image Detection via Large Multimodal Modelscited by 36
  11. 2025D3: Training-Free AI-Generated Video Detection Using Second-Order Featurescited by 34
  12. 2024Contrasting Deepfakes Diffusion via Contrastive Learning and Global-Local Similaritiescited by 27
38 more
  1. 2024Mixture of Low-rank Experts for Transferable AI-Generated Image Detectioncited by 21
  2. 2024SONICS: Synthetic Or Not -- Identifying Counterfeit Songscited by 21
  3. 2024Synthetic Image Verification in the Era of Generative AI: What Works and What Isn't There Yetcited by 13
  4. 2024A Survey of Defenses Against AI-Generated Visual Media: Detection,Disruption, and Authenticationcited by 13
  5. 2025Survey on AI-Generated Media Detection: From Non-MLLM to MLLMcited by 12
  6. 2025Forensic Self-Descriptions Are All You Need for Zero-Shot Detection, Open-Set Source Attribution, and Clustering of AI-generated Imagescited by 11
  7. 2025Manifold Induced Biases for Zero-shot and Few-shot Detection of Generated Imagescited by 10
  8. 2025AI-GenBench: A New Ongoing Benchmark for AI-Generated Image Detectioncited by 10
  9. 2024Examining the Prevalence and Dynamics of AI-Generated Media in Art Subredditscited by 8
  10. 2026Training-free Detection of Generated Videos via Spatial-Temporal Likelihoodscited by 8
  11. 2024Detecting Origin Attribution for Text-to-Image Diffusion Modelscited by 7
  12. 2025Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samplescited by 6
  13. 2026EvoGuard: An Extensible Agentic RL-based Framework for Practical and Evolving AI-Generated Image Detectioncited by 6
  14. 2026PGC: Peak-Guided Calibration for Generalizable AI-Generated Image Detectioncited by 6
  15. 2024DA-HFNet: Progressive Fine-Grained Forgery Image Detection and Localization Based on Dual Attentioncited by 4
  16. 2024The Cat and Mouse Game: The Ongoing Arms Race Between Diffusion Models and Detection Methodscited by 4
  17. 2025AnimeDL-2M: Million-Scale AI-Generated Anime Image Detection and Localization in Diffusion Eracited by 4
  18. 2025Towards Reliable Identification of Diffusion-based Image Manipulationscited by 4
  19. 2024Synthetic Photography Detection: A Visual Guidance for Identifying Synthetic Images Created by AIcited by 3
  20. 2024Detecting the Undetectable: Combining Kolmogorov-Arnold Networks and MLP for AI-Generated Image Detectioncited by 3
  21. 2025DiffSeg30k: A Multi-Turn Diffusion Editing Benchmark for Localized AIGC Detectioncited by 3
  22. 2026Generalizable and Adaptive Continual Learning Framework for AI-generated Image Detectioncited by 3
  23. 2024Human vs. AI: A Novel Benchmark and a Comparative Study on the Detection of Generated Images and the Impact of Promptscited by 2
  24. 2025Hierarchical and Step-Layer-Wise Tuning of Attention Specialty for Multi-Instance Synthesis in Diffusion Transformerscited by 2
  25. 2025"Humor, Art, or Misinformation?": A Multimodal Dataset for Intent-Aware Synthetic Image Detectioncited by 2
  26. 2025Generalized Design Choices for Deepfake Detectorscited by 2
  27. 2025Rethinking the Use of Vision Transformers for AI-Generated Image Detectioncited by 2
  28. 2025Detection of AI Generated Images Using Combined Uncertainty Measures and Particle Swarm Optimised Rejection Mechanismcited by 2
  29. 2024Log-normal Mutations and their Use in Detecting Surreptitious Fake Imagescited by 1
  30. 2025Identifying Prompted Artist Names from Generated Imagescited by 1
  31. 2025Deepfakes: we need to re-think the concept of "real" imagescited by 1
  32. 2025Supervised Contrastive Learning for Few-Shot AI-Generated Image Detection and Attributioncited by 1
  33. 2026Synthetic Image Detection with CLIP: Understanding and Assessing Predictive Cuescited by 1
  34. 2026Images as Tables: In-Context Learning with TabPFN for Low-Data Detection of AI-Generated Imagescited by 1
  35. 2024Towards More Accurate Fake Detection on Images Generated from Advanced Generative and Neural Rendering Modelscited by 0
  36. 2025Beyond Known Fakes: Generalized Detection of AI-Generated Images via Post-hoc Distribution Alignmentcited by 0
  37. 2025FUSE: Unifying Spectral and Semantic Cues for Robust AI-Generated Image Detectioncited by 0
  38. 2026Combating Pattern and Content Bias: Adversarial Feature Learning for Generalized AI-Generated Image Detectioncited by 0

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