mlsanityData to 14 Oct 2026

ID-Reveal: Identity-aware DeepFake Video Detection

arXiv 2012.02512 ↗First posted 4 Dec 2020cs.CVcited by 97

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Abstract (arXiv)

A major challenge in DeepFake forgery detection is that state-of-the-art algorithms are mostly trained to detect a specific fake method. As a result, these approaches show poor generalization across different types of facial manipulations, e.g., from face swapping to facial reenactment. To this end, we introduce ID-Reveal, a new approach that learns temporal facial features, specific of how a person moves while talking, by means of metric learning coupled with an adversarial training strategy. The advantage is that we do not need any training data of fakes, but only train on real videos. Moreover, we utilize high-level semantic features, which enables robustness to widespread and disruptive forms of post-processing. We perform a thorough experimental analysis on several publicly available benchmarks. Compared to state of the art, our method improves generalization and is more robust to low-quality videos, that are usually spread over social networks. In particular, we obtain an average improvement of more than 15% in terms of accuracy for facial reenactment on high compressed videos.

Key facts. Cited by 97 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 Dec 2020.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 10; 2022: 17; 2023: 25; 2024: 18; 2025: 17; 2026: 10.

97 papers.

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  1. 2021Advances in Neural Renderingcited by 365
  2. 2021Neural Head Avatars from Monocular RGB Videoscited by 213
  3. 2022Leveraging Real Talking Faces via Self-Supervision for Robust Forgery Detectioncited by 130
  4. 2022Implicit Identity Leakage: The Stumbling Block to Improving Deepfake Detection Generalizationcited by 123
  5. 2022Semantic-Aware Implicit Neural Audio-Driven Video Portrait Generationcited by 119
  6. 2023TALL: Thumbnail Layout for Deepfake Video Detectioncited by 100
  7. 2022Protecting Celebrities from DeepFake with Identity Consistency Transformercited by 95
  8. 2021Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forwardcited by 82
  9. 2023Efficient Emotional Adaptation for Audio-Driven Talking-Head Generationcited by 81
  10. 2023AltFreezing for More General Video Face Forgery Detectioncited by 78
  11. 2023BlendFace: Re-designing Identity Encoders for Face-Swappingcited by 66
  12. 2021Countering Malicious DeepFakes: Survey, Battleground, and Horizoncited by 56
38 more
  1. 2023Portrait4D: Learning One-Shot 4D Head Avatar Synthesis using Synthetic Datacited by 55
  2. 2023Multimodal Automated Fact-Checking: A Surveycited by 53
  3. 2022Audio-Visual Person-of-Interest DeepFake Detectioncited by 43
  4. 2024On Learning Multi-Modal Forgery Representation for Diffusion Generated Video Detectioncited by 40
  5. 2023Robustness and Generalizability of Deepfake Detection: A Study with Diffusion Modelscited by 36
  6. 2025BusterX: MLLM-Powered AI-Generated Video Forgery Detection and Explanationcited by 33
  7. 2023Parents and Children: Distinguishing Multimodal DeepFakes from Natural Imagescited by 29
  8. 2021COSMOS: Catching Out-of-Context Misinformation with Self-Supervised Learningcited by 27
  9. 2021TAFIM: Targeted Adversarial Attacks against Facial Image Manipulationscited by 26
  10. 2023Locate and Verify: A Two-Stream Network for Improved Deepfake Detectioncited by 26
  11. 2022MINTIME: Multi-Identity Size-Invariant Video Deepfake Detectioncited by 23
  12. 2024Towards a Universal Synthetic Video Detector: From Face or Background Manipulations to Fully AI-Generated Contentcited by 22
  13. 2023Generalizable One-shot Neural Head Avatarcited by 21
  14. 2024Learning Spatiotemporal Inconsistency via Thumbnail Layout for Face Deepfake Detectioncited by 21
  15. 20223D-Aware Video Generationcited by 17
  16. 2021Watch Those Words: Video Falsification Detection Using Word-Conditioned Facial Motioncited by 15
  17. 2025From Specificity to Generality: Revisiting Generalizable Artifacts in Detecting Face Deepfakescited by 14
  18. 2022Analyzing Fairness in Deepfake Detection With Massively Annotated Databasescited by 13
  19. 2021An Experimental Evaluation on Deepfake Detection using Deep Face Recognitioncited by 12
  20. 2023Detecting Images Generated by Diffuserscited by 12
  21. 2021Differential Anomaly Detection for Facial Imagescited by 11
  22. 2022Deepfake audio detection by speaker verificationcited by 11
  23. 2023Learning Pairwise Interaction for Generalizable DeepFake Detectioncited by 11
  24. 2023Avatar Fingerprinting for Authorized Use of Synthetic Talking-Head Videoscited by 11
  25. 2024Deepfake Media Generation and Detection in the Generative AI Era: A Survey and Outlookcited by 10
  26. 2025ExDDV: A New Dataset for Explainable Deepfake Detection in Videocited by 10
  27. 2023Unsupervised Multimodal Deepfake Detection Using Intra- and Cross-Modal Inconsistenciescited by 8
  28. 2024Diffusion Deepfakecited by 8
  29. 2024Training-Free Deepfake Voice Recognition by Leveraging Large-Scale Pre-Trained Modelscited by 8
  30. 2022An Overview of Recent Work in Media Forensics: Methods and Threatscited by 7
  31. 2025Guard Me If You Know Me: Protecting Specific Face-Identity from Deepfakescited by 7
  32. 2022Combining Automatic Speaker Verification and Prosody Analysis for Synthetic Speech Detectioncited by 6
  33. 2023Diff-ID: An Explainable Identity Difference Quantification Framework for DeepFake Detectioncited by 6
  34. 2024Evolving from Single-modal to Multi-modal Facial Deepfake Detection: Progress and Challengescited by 6
  35. 2022DepthFake: a depth-based strategy for detecting Deepfake videoscited by 5
  36. 2023RetouchingFFHQ: A Large-scale Dataset for Fine-grained Face Retouching Detectioncited by 5
  37. 2023A Large-Scale 3D Face Mesh Video Dataset via Neural Re-parameterized Optimizationcited by 5
  38. 2022Exploring Spatial-Temporal Features for Deepfake Detection and Localizationcited by 4

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