mlsanityData to 7 Oct 2026

Are GAN generated images easy to detect? A critical analysis of the state-of-the-art

arXiv 2104.02617 ↗First posted 6 Apr 2021cs.CV cs.AIcited by 139

Not on the map. Why?

Cited by 25 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

The advent of deep learning has brought a significant improvement in the quality of generated media. However, with the increased level of photorealism, synthetic media are becoming hardly distinguishable from real ones, raising serious concerns about the spread of fake or manipulated information over the Internet. In this context, it is important to develop automated tools to reliably and timely detect synthetic media. In this work, we analyze the state-of-the-art methods for the detection of synthetic images, highlighting the key ingredients of the most successful approaches, and comparing their performance over existing generative architectures. We will devote special attention to realistic and challenging scenarios, like media uploaded on social networks or generated by new and unseen architectures, analyzing the impact of suitable augmentation and training strategies on the detectors' generalization ability.

Key facts. Cited by 139 papers in the map (data through 7 Oct 2026). Builds on 23 of its 33 references that are in the map. First posted on arXiv 6 Apr 2021.

Builds on

23 of 33 references.

References · 23

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  2. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  3. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,580
  4. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,967
  5. 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,609
  6. 2019Semantic Image Synthesis with Spatially-Adaptive Normalizationcited by 1,295
  7. 2017StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translationcited by 1,036
  8. 2019CNN-generated images are surprisingly easy to spot... for nowcited by 842
  9. 2020Leveraging Frequency Analysis for Deep Fake Image Recognitioncited by 501
  10. 2019Detecting and Simulating Artifacts in GAN Fake Imagescited by 293
  11. 2020Watch your Up-Convolution: CNN Based Generative Deep Neural Networks are Failing to Reproduce Spectral Distributionscited by 278
  12. 2020What makes fake images detectable? Understanding properties that generalizecited by 272
11 more
  1. 2020Media Forensics and DeepFakes: an overviewcited by 208
  2. 2018Do GANs leave artificial fingerprints?cited by 188
  3. 2019Detecting GAN generated Fake Images using Co-occurrence Matricescited by 124
  4. 2018ForensicTransfer: Weakly-supervised Domain Adaptation for Forgery Detectioncited by 104
  5. 2019Incremental learning for the detection and classification of GAN-generated imagescited by 58
  6. 2019RelGAN: Multi-Domain Image-to-Image Translation via Relative Attributescited by 56
  7. 2020CNN Detection of GAN-Generated Face Images based on Cross-Band Co-occurrences Analysiscited by 42
  8. 2019On the generalization of GAN image forensicscited by 36
  9. 2018Identification of Deep Network Generated Images Using Disparities in Color Componentscited by 18
  10. 2019Towards Generalizable Deepfake Detection with Locality-aware AutoEncodercited by 17
  11. 2019A Full-Image Full-Resolution End-to-End-Trainable CNN Framework for Image Forgery Detectioncited by 15

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 13; 2023: 28; 2024: 41; 2025: 29; 2026: 24.

139 papers.

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  1. 2023The Stable Signature: Rooting Watermarks in Latent Diffusion Modelscited by 289
  2. 2022On the detection of synthetic images generated by diffusion modelscited by 282
  3. 2023Raising the Bar of AI-generated Image Detection with CLIPcited by 163
  4. 2024AEROBLADE: Training-Free Detection of Latent Diffusion Images Using Autoencoder Reconstruction Errorcited by 123
  5. 2023Intriguing properties of synthetic images: from generative adversarial networks to diffusion modelscited by 121
  6. 2023PatchCraft: Exploring Texture Patch for Efficient AI-generated Image Detectioncited by 119
  7. 2022Towards the Detection of Diffusion Model Deepfakescited by 111
  8. 2022Fusing Global and Local Features for Generalized AI-Synthesized Image Detectioncited by 88
  9. 2024Can ChatGPT Detect DeepFakes? A Study of Using Multimodal Large Language Models for Media Forensicscited by 68
  10. 2024A Bias-Free Training Paradigm for More General AI-generated Image Detectioncited by 67
  11. 2022Data Feedback Loops: Model-driven Amplification of Dataset Biasescited by 59
  12. 2024Zero-Shot Detection of AI-Generated Imagescited by 50
38 more
  1. 2023Generalizable Synthetic Image Detection via Language-guided Contrastive Learningcited by 48
  2. 2024CLIPping the Deception: Adapting Vision-Language Models for Universal Deepfake Detectioncited by 47
  3. 2022Detecting GAN-generated Images by Orthogonal Training of Multiple CNNscited by 46
  4. 2023AutoSplice: A Text-prompt Manipulated Image Dataset for Media Forensicscited by 46
  5. 2024Fake or JPEG? Revealing Common Biases in Generated Image Detection Datasetscited by 43
  6. 2024On Learning Multi-Modal Forgery Representation for Diffusion Generated Video Detectioncited by 40
  7. 2024FakeBench: Probing Explainable Fake Image Detection via Large Multimodal Modelscited by 36
  8. 2024RIGID: A Training-free and Model-Agnostic Framework for Robust AI-Generated Image Detectioncited by 36
  9. 2024Aligned Datasets Improve Detection of Latent Diffusion-Generated Imagescited by 36
  10. 2023ArtiFact: A Large-Scale Dataset with Artificial and Factual Images for Generalizable and Robust Synthetic Image Detectioncited by 35
  11. 2022GAN-generated Faces Detection: A Survey and New Perspectivescited by 29
  12. 2023Deep Image Fingerprint: Towards Low Budget Synthetic Image Detection and Model Lineage Analysiscited by 29
  13. 2023Parents and Children: Distinguishing Multimodal DeepFakes from Natural Imagescited by 29
  14. 2023Nepotistically Trained Generative-AI Models Collapsecited by 27
  15. 2024Contrasting Deepfakes Diffusion via Contrastive Learning and Global-Local Similaritiescited by 27
  16. 2024LaWa: Using Latent Space for In-Generation Image Watermarkingcited by 27
  17. 2021Towards Universal GAN Image Detectioncited by 26
  18. 2022GLFF: Global and Local Feature Fusion for AI-synthesized Image Detectioncited by 25
  19. 2023Weakly-supervised deepfake localization in diffusion-generated imagescited by 24
  20. 2024Frequency Masking for Universal Deepfake Detectioncited by 23
  21. 2023Detecting Generated Images by Real Images Onlycited by 20
  22. 2026NTIRE 2026 Challenge on Robust AI-Generated Image Detection in the Wildcited by 18
  23. 2023Improving Synthetically Generated Image Detection in Cross-Concept Settingscited by 15
  24. 2023The Age of Synthetic Realities: Challenges and Opportunitiescited by 15
  25. 2023On the Trustworthiness Landscape of State-of-the-art Generative Models: A Survey and Outlookcited by 15
  26. 2024MiRAGeNews: Multimodal Realistic AI-Generated News Detectioncited by 15
  27. 2023Securing Deep Generative Models with Universal Adversarial Signaturecited by 13
  28. 2024Synthetic Image Verification in the Era of Generative AI: What Works and What Isn't There Yetcited by 13
  29. 2025Few-Shot Learner Generalizes Across AI-Generated Image Detectioncited by 13
  30. 2023Open Set Synthetic Image Source Attributioncited by 12
  31. 2021Forensic Analysis of Synthetically Generated Western Blot Imagescited by 11
  32. 2024Exploring the Adversarial Robustness of CLIP for AI-generated Image Detectioncited by 11
  33. 2025Stay-Positive: A Case for Ignoring Real Image Features in Fake Image Detectioncited by 11
  34. 2024AI-Generated Faces in the Real World: A Large-Scale Case Study of Twitter Profile Imagescited by 10
  35. 2024AI-Generated Video Detection via Spatio-Temporal Anomaly Learningcited by 9
  36. 2024TextureCrop: Enhancing Synthetic Image Detection through Texture-based Croppingcited by 9
  37. 2023Finding AI-Generated Faces in the Wildcited by 8
  38. 2023Tracing Hyperparameter Dependencies for Model Parsing via Learnable Graph Pooling Networkcited by 8

50 most cited of 139 All 139 (JSON)