mlsanityData to 7 Oct 2026

Deep Learning for Deepfakes Creation and Detection: A Survey

arXiv 1909.11573 ↗First posted 25 Sep 2019cs.CV cs.LG eess.IVcited by 102

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Abstract (arXiv)

Deep learning has been successfully applied to solve various complex problems ranging from big data analytics to computer vision and human-level control. Deep learning advances however have also been employed to create software that can cause threats to privacy, democracy and national security. One of those deep learning-powered applications recently emerged is deepfake. Deepfake algorithms can create fake images and videos that humans cannot distinguish them from authentic ones. The proposal of technologies that can automatically detect and assess the integrity of digital visual media is therefore indispensable. This paper presents a survey of algorithms used to create deepfakes and, more importantly, methods proposed to detect deepfakes in the literature to date. We present extensive discussions on challenges, research trends and directions related to deepfake technologies. By reviewing the background of deepfakes and state-of-the-art deepfake detection methods, this study provides a comprehensive overview of deepfake techniques and facilitates the development of new and more robust methods to deal with the increasingly challenging deepfakes.

Key facts. Cited by 102 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Sep 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 15; 2022: 16; 2023: 20; 2024: 21; 2025: 18; 2026: 12.

102 papers.

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  1. 2022LION: Latent Point Diffusion Models for 3D Shape Generationcited by 552
  2. 2023Character-LLM: A Trainable Agent for Role-Playingcited by 335
  3. 2021Score-Based Generative Modeling with Critically-Damped Langevin Diffusioncited by 230
  4. 2021Trustworthy AI: From Principles to Practicescited by 206
  5. 2021EditGAN: High-Precision Semantic Image Editingcited by 156
  6. 2022GENIE: Higher-Order Denoising Diffusion Solverscited by 127
  7. 2022Differentially Private Diffusion Modelscited by 118
  8. 2024Align Your Steps: Optimizing Sampling Schedules in Diffusion Modelscited by 91
  9. 2021Towards Measuring Fairness in AI: the Casual Conversations Datasetcited by 80
  10. 2021Intermediate Layer Optimization for Inverse Problems using Deep Generative Modelscited by 70
  11. 2021CCVS: Context-aware Controllable Video Synthesiscited by 68
  12. 2021Rebooting ACGAN: Auxiliary Classifier GANs with Stable Trainingcited by 57
38 more
  1. 2024IMDL-BenCo: A Comprehensive Benchmark and Codebase for Image Manipulation Detection & Localizationcited by 42
  2. 2022StyleSwap: Style-Based Generator Empowers Robust Face Swappingcited by 39
  3. 2023Qualitative Failures of Image Generation Models and Their Application in Detecting Deepfakescited by 31
  4. 2022GAN-generated Faces Detection: A Survey and New Perspectivescited by 29
  5. 2022Face Generation and Editing with StyleGAN: A Surveycited by 27
  6. 2023Weakly-supervised deepfake localization in diffusion-generated imagescited by 24
  7. 2021Deepfake Detection Scheme Based on Vision Transformer and Distillationcited by 22
  8. 2023BakedAvatar: Baking Neural Fields for Real-Time Head Avatar Synthesiscited by 20
  9. 2024ManiFPT: Defining and Analyzing Fingerprints of Generative Modelscited by 18
  10. 2024The AI Community Building the Future? A Quantitative Analysis of Development Activity on Hugging Face Hubcited by 18
  11. 2024DeCLIP: Decoding CLIP representations for deepfake localizationcited by 17
  12. 2023VIVE3D: Viewpoint-Independent Video Editing using 3D-Aware GANscited by 16
  13. 2024Exposing Lip-syncing Deepfakes from Mouth Inconsistenciescited by 15
  14. 2024SoK: Systematization and Benchmarking of Deepfake Detectors in a Unified Frameworkcited by 14
  15. 20233D-aware Blending with Generative NeRFscited by 13
  16. 2022A Review of Deep Learning-based Approaches for Deepfake Content Detectioncited by 12
  17. 2023GenConViT: Deepfake Video Detection Using Generative Convolutional Vision Transformercited by 12
  18. 2024DiMoDif: Discourse Modality-information Differentiation for Audio-visual Deepfake Detection and Localizationcited by 12
  19. 2021MixerGAN: An MLP-Based Architecture for Unpaired Image-to-Image Translationcited by 10
  20. 2023Single-Model Attribution of Generative Models Through Final-Layer Inversioncited by 10
  21. 2024A Timely Survey on Vision Transformer for Deepfake Detectioncited by 10
  22. 2023Deepfake detection by exploiting surface anomalies: the SurFake approachcited by 9
  23. 2023A Statistical Turing Test for Generative Modelscited by 8
  24. 2022A Hybrid CNN-LSTM model for Video Deepfake Detection by Leveraging Optical Flow Featurescited by 7
  25. 2024Unmasking Illusions: Understanding Human Perception of Audiovisual Deepfakescited by 7
  26. 2024Image-Perfect Imperfections: Safety, Bias, and Authenticity in the Shadow of Text-To-Image Model Evolutioncited by 7
  27. 2025Detecting Lip-Syncing Deepfakes: Vision Temporal Transformer for Analyzing Mouth Inconsistenciescited by 7
  28. 2025Riemannian-Geometric Fingerprints of Generative Modelscited by 7
  29. 2021How Deep Are the Fakes? Focusing on Audio Deepfake: A Surveycited by 6
  30. 2022Deepfake: Definitions, Performance Metrics and Standards, Datasets and Benchmarks, and a Meta-Reviewcited by 6
  31. 2022M-VADER: A Model for Diffusion with Multimodal Contextcited by 6
  32. 2023CrossDF: Improving Cross-Domain Deepfake Detection with Deep Information Decompositioncited by 6
  33. 2023FakeTracer: Catching Face-swap DeepFakes via Implanting Traces in Trainingcited by 5
  34. 2024Social Media Authentication and Combating Deepfakes using Semi-fragile Invisible Image Watermarkingcited by 5
  35. 2025Robust AI-Generated Face Detection with Imbalanced Datacited by 5
  36. 2025Multiverse Through Deepfakes: The MultiFakeVerse Dataset of Person-Centric Visual and Conceptual Manipulationscited by 5
  37. 2025SpecXNet: A Dual-Domain Convolutional Network for Robust Deepfake Detectioncited by 5
  38. 2025PRPO: Paragraph-level Policy Optimization for Vision-Language Deepfake Detectioncited by 5

50 most cited of 102 All 102 (JSON)