mlsanityData to 14 Oct 2026

Exposing DeepFake Videos By Detecting Face Warping Artifacts

arXiv 1811.00656 ↗First posted 1 Nov 2018cs.CVcited by 319

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Abstract (arXiv)

In this work, we describe a new deep learning based method that can effectively distinguish AI-generated fake videos (referred to as em DeepFake videos hereafter) from real videos. Our method is based on the observations that current DeepFake algorithm can only generate images of limited resolutions, which need to be further warped to match the original faces in the source video. Such transforms leave distinctive artifacts in the resulting DeepFake videos, and we show that they can be effectively captured by convolutional neural networks (CNNs). Compared to previous methods which use a large amount of real and DeepFake generated images to train CNN classifier, our method does not need DeepFake generated images as negative training examples since we target the artifacts in affine face warping as the distinctive feature to distinguish real and fake images. The advantages of our method are two-fold: (1) Such artifacts can be simulated directly using simple image processing operations on a image to make it as negative example. Since training a DeepFake model to generate negative examples is time-consuming and resource-demanding, our method saves a plenty of time and resources in training data collection; (2) Since such artifacts are general existed in DeepFake videos from different sources, our method is more robust compared to others. Our method is evaluated on two sets of DeepFake video datasets for its effectiveness in practice.

Key facts. Cited by 319 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Nov 2018.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 56; 2022: 66; 2023: 53; 2024: 57; 2025: 51; 2026: 36.

319 papers.

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  1. 2021Multi-attentional Deepfake Detectioncited by 341
  2. 2022Detecting Deepfakes with Self-Blended Imagescited by 338
  3. 2023The Stable Signature: Rooting Watermarks in Latent Diffusion Modelscited by 304
  4. 2021Generalizing Face Forgery Detection with High-frequency Featurescited by 262
  5. 2021Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domaincited by 244
  6. 2021WildDeepfake: A Challenging Real-World Dataset for Deepfake Detectioncited by 209
  7. 2023UCF: Uncovering Common Features for Generalizable Deepfake Detectioncited by 180
  8. 2022Self-supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detectioncited by 173
  9. 2023DeepfakeBench: A Comprehensive Benchmark of Deepfake Detectioncited by 166
  10. 2021Exploring Temporal Coherence for More General Video Face Forgery Detectioncited by 159
  11. 2021Frequency-aware Discriminative Feature Learning Supervised by Single-Center Loss for Face Forgery Detectioncited 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 129
  2. 2023Transcending Forgery Specificity with Latent Space Augmentation for Generalizable Deepfake Detectioncited by 127
  3. 2022Implicit Identity Leakage: The Stumbling Block to Improving Deepfake Detection Generalizationcited by 123
  4. 2021M2TR: Multi-modal Multi-scale Transformers for Deepfake Detectioncited by 114
  5. 2023TALL: Thumbnail Layout for Deepfake Video Detectioncited by 100
  6. 2022Protecting Celebrities from DeepFake with Identity Consistency Transformercited by 95
  7. 2021Spatiotemporal Inconsistency Learning for DeepFake Video Detectioncited by 88
  8. 2021Face Forensics in the Wildcited by 87
  9. 2023Self-Supervised Video Forensics by Audio-Visual Anomaly Detectioncited by 85
  10. 2021Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forwardcited by 82
  11. 2023AV-Deepfake1M: A Large-Scale LLM-Driven Audio-Visual Deepfake Datasetcited by 80
  12. 2021Deepfake Video Detection Using Convolutional Vision Transformercited by 79
  13. 2021WaveFake: A Data Set to Facilitate Audio Deepfake Detectioncited by 78
  14. 2023AltFreezing for More General Video Face Forgery Detectioncited by 78
  15. 2022FrePGAN: Robust Deepfake Detection Using Frequency-level Perturbationscited by 75
  16. 2022CORE: Consistent Representation Learning for Face Forgery Detectioncited by 74
  17. 2022UIA-ViT: Unsupervised Inconsistency-Aware Method based on Vision Transformer for Face Forgery Detectioncited by 69
  18. 2024Can ChatGPT Detect DeepFakes? A Study of Using Multimodal Large Language Models for Media Forensicscited by 69
  19. 2025Spot the Fake: Large Multimodal Model-Based Synthetic Image Detection with Artifact Explanationcited by 68
  20. 2024Deepfake Generation and Detection: A Benchmark and Surveycited by 67
  21. 2024Exploiting Style Latent Flows for Generalizing Deepfake Video Detectioncited by 59
  22. 2024Generalizing Deepfake Video Detection with Plug-and-Play: Video-Level Blending and Spatiotemporal Adapter Tuningcited by 59
  23. 2021Reverse Engineering of Generative Models: Inferring Model Hyperparameters from Generated Imagescited by 58
  24. 2021Improving the Efficiency and Robustness of Deepfakes Detection through Precise Geometric Featurescited by 57
  25. 2021Countering Malicious DeepFakes: Survey, Battleground, and Horizoncited by 56
  26. 2021OpenForensics: Large-Scale Challenging Dataset For Multi-Face Forgery Detection And Segmentation In-The-Wildcited by 50
  27. 2022Deepfake Detection: A Comprehensive Survey from the Reliability Perspectivecited by 50
  28. 2023Beyond the Prior Forgery Knowledge: Mining Critical Clues for General Face Forgery Detectioncited by 50
  29. 2022SeeABLE: Soft Discrepancies and Bounded Contrastive Learning for Exposing Deepfakescited by 49
  30. 2024Can We Leave Deepfake Data Behind in Training Deepfake Detector?cited by 49
  31. 2021ADD: Frequency Attention and Multi-View based Knowledge Distillation to Detect Low-Quality Compressed Deepfake Imagescited by 48
  32. 2024FreqBlender: Enhancing DeepFake Detection by Blending Frequency Knowledgecited by 48
  33. 2024Exposing the Deception: Uncovering More Forgery Clues for Deepfake Detectioncited by 47
  34. 2022A Continual Deepfake Detection Benchmark: Dataset, Methods, and Essentialscited by 45
  35. 2021FReTAL: Generalizing Deepfake Detection using Knowledge Distillation and Representation Learningcited by 44
  36. 2022Voice-Face Homogeneity Tells Deepfakecited by 44
  37. 2023SepMark: Deep Separable Watermarking for Unified Source Tracing and Deepfake Detectioncited by 44
  38. 2023Glitch in the Matrix: A Large Scale Benchmark for Content Driven Audio-Visual Forgery Detection and Localizationcited by 43

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