mlsanityData to 7 Oct 2026

Detecting and Simulating Artifacts in GAN Fake Images

arXiv 1907.06515 ↗First posted 15 Jul 2019cs.CV eess.IVcited by 293

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Cited by 46 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

To detect GAN generated images, conventional supervised machine learning algorithms require collection of a number of real and fake images from the targeted GAN model. However, the specific model used by the attacker is often unavailable. To address this, we propose a GAN simulator, AutoGAN, which can simulate the artifacts produced by the common pipeline shared by several popular GAN models. Additionally, we identify a unique artifact caused by the up-sampling component included in the common GAN pipeline. We show theoretically such artifacts are manifested as replications of spectra in the frequency domain and thus propose a classifier model based on the spectrum input, rather than the pixel input. By using the simulated images to train a spectrum based classifier, even without seeing the fake images produced by the targeted GAN model during training, our approach achieves state-of-the-art performances on detecting fake images generated by popular GAN models such as CycleGAN.

Key facts. Cited by 293 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Jul 2019.

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Citing papers per year: 2021: 47; 2022: 34; 2023: 46; 2024: 56; 2025: 69; 2026: 41.

293 papers.

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  1. 2023Towards Universal Fake Image Detectors that Generalize Across Generative Modelscited by 483
  2. 2023DIRE for Diffusion-Generated Image Detectioncited by 386
  3. 2023GenImage: A Million-Scale Benchmark for Detecting AI-Generated Imagecited by 318
  4. 2023The Stable Signature: Rooting Watermarks in Latent Diffusion Modelscited by 289
  5. 2022On the detection of synthetic images generated by diffusion modelscited by 282
  6. 2024A Sanity Check for AI-generated Image Detectioncited by 187
  7. 2022DE-FAKE: Detection and Attribution of Fake Images Generated by Text-to-Image Generation Modelscited by 167
  8. 2023Raising the Bar of AI-generated Image Detection with CLIPcited by 163
  9. 2021Exploring Temporal Coherence for More General Video Face Forgery Detectioncited by 158
  10. 2021Are GAN generated images easy to detect? A critical analysis of the state-of-the-artcited by 139
  11. 2023Hierarchical Fine-Grained Image Forgery Detection and Localizationcited by 134
  12. 2022Leveraging Real Talking Faces via Self-Supervision for Robust Forgery Detectioncited by 130
38 more
  1. 2024DF40: Toward Next-Generation Deepfake Detectioncited by 126
  2. 2023Intriguing properties of synthetic images: from generative adversarial networks to diffusion modelscited by 121
  3. 2022Towards the Detection of Diffusion Model Deepfakescited by 111
  4. 2024C2P-CLIP: Injecting Category Common Prompt in CLIP to Enhance Generalization in Deepfake Detectioncited by 111
  5. 2021Representative Forgery Mining for Fake Face Detectioncited by 108
  6. 2024Orthogonal Subspace Decomposition for Generalizable AI-Generated Image Detectioncited by 102
  7. 2021Local Relation Learning for Face Forgery Detectioncited by 100
  8. 2023Identifying and Mitigating the Security Risks of Generative AIcited by 90
  9. 2024Leveraging Representations from Intermediate Encoder-blocks for Synthetic Image Detectioncited by 88
  10. 2021Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forwardcited by 82
  11. 2021WaveFake: A Data Set to Facilitate Audio Deepfake Detectioncited by 78
  12. 2022FrePGAN: Robust Deepfake Detection Using Frequency-level Perturbationscited by 75
  13. 2024Preserving Fairness Generalization in Deepfake Detectioncited by 70
  14. 2021A Closer Look at Fourier Spectrum Discrepancies for CNN-generated Images Detectioncited by 68
  15. 2024A Bias-Free Training Paradigm for More General AI-generated Image Detectioncited by 67
  16. 2021Towards Discovery and Attribution of Open-world GAN Generated Imagescited by 66
  17. 2022HDNet: High-resolution Dual-domain Learning for Spectral Compressive Imagingcited by 66
  18. 2023Seeing is not always believing: Benchmarking Human and Model Perception of AI-Generated Imagescited by 65
  19. 2025LEGION: Learning to Ground and Explain for Synthetic Image Detectioncited by 65
  20. 2023AntifakePrompt: Prompt-Tuned Vision-Language Models are Fake Image Detectorscited by 64
  21. 2024FakeInversion: Learning to Detect Images from Unseen Text-to-Image Models by Inverting Stable Diffusioncited by 61
  22. 2021BiHPF: Bilateral High-Pass Filters for Robust Deepfake Detectioncited by 60
  23. 2021On the Frequency Bias of Generative Modelscited by 60
  24. 2021Reverse Engineering of Generative Models: Inferring Model Hyperparameters from Generated Imagescited by 58
  25. 2021Countering Malicious DeepFakes: Survey, Battleground, and Horizoncited by 56
  26. 2021Ensembling with Deep Generative Viewscited by 53
  27. 2024Zero-Shot Detection of AI-Generated Imagescited by 50
  28. 2024Community Forensics: Using Thousands of Generators to Train Fake Image Detectorscited by 50
  29. 2021ADD: Frequency Attention and Multi-View based Knowledge Distillation to Detect Low-Quality Compressed Deepfake Imagescited by 48
  30. 2023Generalizable Synthetic Image Detection via Language-guided Contrastive Learningcited by 48
  31. 2022Deepfake Network Architecture Attributioncited by 47
  32. 2024LaRE2: Latent Reconstruction Error Based Method for Diffusion-Generated Image Detectioncited by 47
  33. 2021Beyond the Spectrum: Detecting Deepfakes via Re-Synthesiscited by 44
  34. 2023Detecting Images Generated by Deep Diffusion Models using their Local Intrinsic Dimensionalitycited by 44
  35. 2023Perceptual Artifacts Localization for Image Synthesis Taskscited by 43
  36. 2024A Single Simple Patch is All You Need for AI-generated Image Detectioncited by 43
  37. 2024Fake or JPEG? Revealing Common Biases in Generated Image Detection Datasetscited by 43
  38. 2024D3: Scaling Up Deepfake Detection by Learning from Discrepancycited by 43

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