mlsanityData to 7 Oct 2026

The Creation and Detection of Deepfakes: A Survey

arXiv 2004.11138 ↗First posted 23 Apr 2020cs.CV cs.LG eess.IVcited by 246

Not on the map. Why?

Cited by 36 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Generative deep learning algorithms have progressed to a point where it is difficult to tell the difference between what is real and what is fake. In 2018, it was discovered how easy it is to use this technology for unethical and malicious applications, such as the spread of misinformation, impersonation of political leaders, and the defamation of innocent individuals. Since then, these `deepfakes' have advanced significantly. In this paper, we explore the creation and detection of deepfakes and provide an in-depth view of how these architectures work. The purpose of this survey is to provide the reader with a deeper understanding of (1) how deepfakes are created and detected, (2) the current trends and advancements in this domain, (3) the shortcomings of the current defense solutions, and (4) the areas which require further research and attention.

Key facts. Cited by 246 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Apr 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 36; 2022: 39; 2023: 43; 2024: 38; 2025: 37; 2026: 53.

246 papers.

Sort:

By area:

  1. 2022LION: Latent Point Diffusion Models for 3D Shape Generationcited by 552
  2. 2022DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Modelscited by 376
  3. 2024MACE: Mass Concept Erasure in Diffusion Modelscited by 298
  4. 2021Score-Based Generative Modeling with Critically-Damped Langevin Diffusioncited by 230
  5. 2023Selective Amnesia: A Continual Learning Approach to Forgetting in Deep Generative Modelscited by 175
  6. 2023Raising the Cost of Malicious AI-Powered Image Editingcited by 160
  7. 2021EditGAN: High-Precision Semantic Image Editingcited by 156
  8. 2023AGIQA-3K: An Open Database for AI-Generated Image Quality Assessmentcited by 142
  9. 2022GAUDI: A Neural Architect for Immersive 3D Scene Generationcited by 136
  10. 2022GENIE: Higher-Order Denoising Diffusion Solverscited by 127
  11. 2023State of the Art on Diffusion Models for Visual Computingcited by 126
  12. 2024DF40: Toward Next-Generation Deepfake Detectioncited by 126
38 more
  1. 2023Factuality Challenges in the Era of Large Language Modelscited by 125
  2. 2022Score-Based Generative Models Detect Manifoldscited by 121
  3. 2023Generative AIcited by 119
  4. 2022Differentially Private Diffusion Modelscited by 118
  5. 2023TALL: Thumbnail Layout for Deepfake Video Detectioncited by 100
  6. 2024Align Your Steps: Optimizing Sampling Schedules in Diffusion Modelscited by 91
  7. 2022Fusing Global and Local Features for Generalized AI-Synthesized Image Detectioncited by 88
  8. 2024Leveraging Representations from Intermediate Encoder-blocks for Synthetic Image Detectioncited by 88
  9. 2021A Survey on Bias in Visual Datasetscited by 84
  10. 2021Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forwardcited by 82
  11. 2021Deepfake Video Detection Using Convolutional Vision Transformercited by 79
  12. 2024Deepfake Generation and Detection: A Benchmark and Surveycited by 66
  13. 2023Seeing is not always believing: Benchmarking Human and Model Perception of AI-Generated Imagescited by 65
  14. 2024Common Sense Reasoning for Deepfake Detectioncited by 64
  15. 2021Deepfake Detection by Human Crowds, Machines, and Machine-informed Crowdscited by 58
  16. 2021Countering Malicious DeepFakes: Survey, Battleground, and Horizoncited by 56
  17. 2021Multimodal Image Synthesis and Editing: The Generative AI Eracited by 52
  18. 2023Leveraging Optimization for Adaptive Attacks on Image Watermarkscited by 50
  19. 2022SeeABLE: Soft Discrepancies and Bounded Contrastive Learning for Exposing Deepfakescited by 49
  20. 2022Deepfake Detection: A Comprehensive Survey from the Reliability Perspectivecited by 48
  21. 2024CLIPping the Deception: Adapting Vision-Language Models for Universal Deepfake Detectioncited by 47
  22. 2022Exploring Frequency Adversarial Attacks for Face Forgery Detectioncited by 45
  23. 2022UVCGAN: UNet Vision Transformer cycle-consistent GAN for unpaired image-to-image translationcited by 42
  24. 2023Glitch in the Matrix: A Large Scale Benchmark for Content Driven Audio-Visual Forgery Detection and Localizationcited by 42
  25. 2022Attribution and Obfuscation of Neural Text Authorship: A Data Mining Perspectivecited by 40
  26. 2021The Threat of Offensive AI to Organizationscited by 37
  27. 2021CoReD: Generalizing Fake Media Detection with Continual Representation using Distillationcited by 37
  28. 2021An Examination of Fairness of AI Models for Deepfake Detectioncited by 34
  29. 2023PTW: Pivotal Tuning Watermarking for Pre-Trained Image Generatorscited by 34
  30. 2021One Detector to Rule Them All: Towards a General Deepfake Attack Detection Frameworkcited by 33
  31. 2021Video Transformer for Deepfake Detection with Incremental Learningcited by 33
  32. 2025ViMo: A Generative Visual GUI World Model for App Agentscited by 33
  33. 2021Learned Spatial Representations for Few-shot Talking-Head Synthesiscited by 32
  34. 2022GAN-generated Faces Detection: A Survey and New Perspectivescited by 29
  35. 2021Neural Emotion Director: Speech-preserving semantic control of facial expressions in "in-the-wild" videoscited by 28
  36. 2022Synthetic Data in Human Analysis: A Surveycited by 28
  37. 2023Diffusion Probabilistic Fieldscited by 28
  38. 2022Face Generation and Editing with StyleGAN: A Surveycited by 27

50 most cited of 246 All 246 (JSON)