mlsanityData to 7 Oct 2026

DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection

arXiv 2001.00179 ↗First posted 1 Jan 2020cs.CV cs.MMcited by 231

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Abstract (arXiv)

The free access to large-scale public databases, together with the fast progress of deep learning techniques, in particular Generative Adversarial Networks, have led to the generation of very realistic fake content with its corresponding implications towards society in this era of fake news. This survey provides a thorough review of techniques for manipulating face images including DeepFake methods, and methods to detect such manipulations. In particular, four types of facial manipulation are reviewed: i) entire face synthesis, ii) identity swap (DeepFakes), iii) attribute manipulation, and iv) expression swap. For each manipulation group, we provide details regarding manipulation techniques, existing public databases, and key benchmarks for technology evaluation of fake detection methods, including a summary of results from those evaluations. Among all the aspects discussed in the survey, we pay special attention to the latest generation of DeepFakes, highlighting its improvements and challenges for fake detection. In addition to the survey information, we also discuss open issues and future trends that should be considered to advance in the field.

Key facts. Cited by 231 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Jan 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 55; 2022: 50; 2023: 29; 2024: 28; 2025: 36; 2026: 33.

231 papers.

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  1. 2021Neural Fields in Visual Computing and Beyondcited by 539
  2. 2021FakeAVCeleb: A Novel Audio-Video Multimodal Deepfake Datasetcited by 179
  3. 2023DeepfakeBench: A Comprehensive Benchmark of Deepfake Detectioncited by 162
  4. 2021PSCC-Net: Progressive Spatio-Channel Correlation Network for Image Manipulation Detection and Localizationcited by 133
  5. 2021Advances in adversarial attacks and defenses in computer vision: A surveycited by 127
  6. 2023State of the Art on Diffusion Models for Visual Computingcited by 126
  7. 2021Pros and Cons of GAN Evaluation Measures: New Developmentscited by 121
  8. 2022Semantic-Aware Implicit Neural Audio-Driven Video Portrait Generationcited by 119
  9. 2021Representative Forgery Mining for Fake Face Detectioncited by 108
  10. 2024Leveraging Representations from Intermediate Encoder-blocks for Synthetic Image Detectioncited by 88
  11. 2021Face Forensics in the Wildcited by 87
  12. 2021Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forwardcited by 82
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  1. 2021Combining EfficientNet and Vision Transformers for Video Deepfake Detectioncited by 81
  2. 2021KoDF: A Large-scale Korean DeepFake Detection Datasetcited by 80
  3. 2021Deepfake Video Detection Using Convolutional Vision Transformercited by 79
  4. 2022FrePGAN: Robust Deepfake Detection Using Frequency-level Perturbationscited by 75
  5. 2024SNIFFER: Multimodal Large Language Model for Explainable Out-of-Context Misinformation Detectioncited by 73
  6. 2021Deep Learning for Face Anti-Spoofing: A Surveycited by 64
  7. 2024Common Sense Reasoning for Deepfake Detectioncited by 64
  8. 2024FFAA: Multimodal Large Language Model based Explainable Open-World Face Forgery Analysis Assistantcited by 59
  9. 2021Deepfake Detection by Human Crowds, Machines, and Machine-informed Crowdscited by 58
  10. 2021Countering Malicious DeepFakes: Survey, Battleground, and Horizoncited by 56
  11. 2022MARLIN: Masked Autoencoder for facial video Representation LearnINgcited by 55
  12. 2021CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakescited by 54
  13. 2022SeeABLE: Soft Discrepancies and Bounded Contrastive Learning for Exposing Deepfakescited by 49
  14. 2022Deepfake Detection: A Comprehensive Survey from the Reliability Perspectivecited by 48
  15. 2023Towards General Visual-Linguistic Face Forgery Detectioncited by 45
  16. 2023Learning Personalized High Quality Volumetric Head Avatars from Monocular RGB Videoscited by 44
  17. 2022Audio-Visual Person-of-Interest DeepFake Detectioncited by 43
  18. 2021Fighting deepfakes by detecting GAN DCT anomaliescited by 39
  19. 2023Synthetic Data for Face Recognition: Current State and Future Prospectscited by 38
  20. 2022VoLux-GAN: A Generative Model for 3D Face Synthesis with HDRI Relightingcited by 37
  21. 2022GATSBI: Generative Adversarial Training for Simulation-Based Inferencecited by 36
  22. 2021An Examination of Fairness of AI Models for Deepfake Detectioncited by 34
  23. 2022On Improving Cross-dataset Generalization of Deepfake Detectorscited by 34
  24. 2021Learned Spatial Representations for Few-shot Talking-Head Synthesiscited by 32
  25. 2021Evaluation of an Audio-Video Multimodal Deepfake Dataset using Unimodal and Multimodal Detectorscited by 32
  26. 2022GBDF: Gender Balanced DeepFake Dataset Towards Fair DeepFake Detectioncited by 32
  27. 2021DefakeHop: A Light-Weight High-Performance Deepfake Detectorcited by 31
  28. 2024Recent Advances in Generative AI and Large Language Models: Current Status, Challenges, and Perspectivescited by 30
  29. 2022GAN-generated Faces Detection: A Survey and New Perspectivescited by 29
  30. 2024DiffusionFake: Enhancing Generalization in Deepfake Detection via Guided Stable Diffusioncited by 29
  31. 2021Neural Emotion Director: Speech-preserving semantic control of facial expressions in "in-the-wild" videoscited by 28
  32. 2022Synthetic Data in Human Analysis: A Surveycited by 28
  33. 2023Speech Driven Video Editing via an Audio-Conditioned Diffusion Modelcited by 28
  34. 2023Locate and Verify: A Two-Stream Network for Improved Deepfake Detectioncited by 26
  35. 2021Exploring Adversarial Fake Images on Face Manifoldcited by 24
  36. 2022Benchmarking Joint Face Spoofing and Forgery Detection with Visual and Physiological Cuescited by 24
  37. 2023Towards Generic Image Manipulation Detection with Weakly-Supervised Self-Consistency Learningcited by 23
  38. 2021High Resolution Face Editing with Masked GAN Latent Code Optimizationcited by 21

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