mlsanityData to 7 Oct 2026

Exploring Temporal Coherence for More General Video Face Forgery Detection

arXiv 2108.06693 ↗First posted 15 Aug 2021cs.CVcited by 158

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Abstract (arXiv)

Although current face manipulation techniques achieve impressive performance regarding quality and controllability, they are struggling to generate temporal coherent face videos. In this work, we explore to take full advantage of the temporal coherence for video face forgery detection. To achieve this, we propose a novel end-to-end framework, which consists of two major stages. The first stage is a fully temporal convolution network (FTCN). The key insight of FTCN is to reduce the spatial convolution kernel size to 1, while maintaining the temporal convolution kernel size unchanged. We surprisingly find this special design can benefit the model for extracting the temporal features as well as improve the generalization capability. The second stage is a Temporal Transformer network, which aims to explore the long-term temporal coherence. The proposed framework is general and flexible, which can be directly trained from scratch without any pre-training models or external datasets. Extensive experiments show that our framework outperforms existing methods and remains effective when applied to detect new sorts of face forgery videos.

Key facts. Cited by 158 papers in the map (data through 7 Oct 2026). Builds on 46 of its 59 references that are in the map. First posted on arXiv 15 Aug 2021.

Builds on

46 of 59 references.

References · 46

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34 more
  1. 2020Thinking in Frequency: Face Forgery Detection by Mining Frequency-aware Cluescited by 518
  2. 2020Leveraging Frequency Analysis for Deep Fake Image Recognitioncited by 501
  3. 2019Face X-ray for More General Face Forgery Detectioncited by 469
  4. 2018MesoNet: a Compact Facial Video Forgery Detection Networkcited by 409
  5. 2019FSGAN: Subject Agnostic Face Swapping and Reenactmentcited by 319
  6. 2018Exposing DeepFake Videos By Detecting Face Warping Artifactscited by 318
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  8. 2019Detecting and Simulating Artifacts in GAN Fake Imagescited by 293
  9. 2020Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery Detectioncited by 286
  10. 2020Watch your Up-Convolution: CNN Based Generative Deep Neural Networks are Failing to Reproduce Spectral Distributionscited by 278
  11. 2020What makes fake images detectable? Understanding properties that generalizecited by 272
  12. 2019Deferred Neural Rendering: Image Synthesis using Neural Texturescited by 253
  13. 2020DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detectioncited by 251
  14. 2019Neural Voice Puppetry: Audio-driven Facial Reenactmentcited by 249
  15. 2017Learning Spatio-Temporal Features with 3D Residual Networks for Action Recognitioncited by 178
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  18. 2019Realistic Speech-Driven Facial Animation with GANscited by 142
  19. 2019Multi-task Learning For Detecting and Segmenting Manipulated Facial Images and Videoscited by 130
  20. 2018Two-Stream Neural Networks for Tampered Face Detectioncited by 125
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 21; 2023: 28; 2024: 47; 2025: 38; 2026: 24.

158 papers.

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  1. 2022Detecting Deepfakes with Self-Blended Imagescited by 332
  2. 2023UCF: Uncovering Common Features for Generalizable Deepfake Detectioncited by 177
  3. 2022Leveraging Real Talking Faces via Self-Supervision for Robust Forgery Detectioncited by 130
  4. 2023Transcending Forgery Specificity with Latent Space Augmentation for Generalizable Deepfake Detectioncited by 126
  5. 2023TALL: Thumbnail Layout for Deepfake Video Detectioncited by 100
  6. 2022Protecting Celebrities from DeepFake with Identity Consistency Transformercited by 95
  7. 2024DeMamba: AI-Generated Video Detection on Million-Scale GenVideo Benchmarkcited by 85
  8. 2023Self-Supervised Video Forensics by Audio-Visual Anomaly Detectioncited by 84
  9. 2023AltFreezing for More General Video Face Forgery Detectioncited by 78
  10. 2022CORE: Consistent Representation Learning for Face Forgery Detectioncited by 73
  11. 2022UIA-ViT: Unsupervised Inconsistency-Aware Method based on Vision Transformer for Face Forgery Detectioncited by 69
  12. 2023BlendFace: Re-designing Identity Encoders for Face-Swappingcited by 66
38 more
  1. 2024Deepfake Generation and Detection: A Benchmark and Surveycited by 66
  2. 2024AVFF: Audio-Visual Feature Fusion for Video Deepfake Detectioncited by 59
  3. 2024Exploiting Style Latent Flows for Generalizing Deepfake Video Detectioncited by 58
  4. 2024Generalizing Deepfake Video Detection with Plug-and-Play: Video-Level Blending and Spatiotemporal Adapter Tuningcited by 57
  5. 2023Multimodal Automated Fact-Checking: A Surveycited by 51
  6. 2023Beyond the Prior Forgery Knowledge: Mining Critical Clues for General Face Forgery Detectioncited by 50
  7. 2022SeeABLE: Soft Discrepancies and Bounded Contrastive Learning for Exposing Deepfakescited by 49
  8. 2024FreqBlender: Enhancing DeepFake Detection by Blending Frequency Knowledgecited by 48
  9. 2024Can We Leave Deepfake Data Behind in Training Deepfake Detector?cited by 48
  10. 2024Exposing the Deception: Uncovering More Forgery Clues for Deepfake Detectioncited by 47
  11. 2022Audio-Visual Person-of-Interest DeepFake Detectioncited by 43
  12. 2022Delving into Sequential Patches for Deepfake Detectioncited by 41
  13. 2024On Learning Multi-Modal Forgery Representation for Diffusion Generated Video Detectioncited by 40
  14. 2025Deepfake-Eval-2024: A Multi-Modal In-the-Wild Benchmark of Deepfakes Circulated in 2024cited by 40
  15. 2025Rethinking Vision-Language Model in Face Forensics: Multi-Modal Interpretable Forged Face Detectorcited by 38
  16. 2024Towards More General Video-based Deepfake Detection through Facial Component Guided Adaptation for Foundation Modelcited by 37
  17. 2023Robustness and Generalizability of Deepfake Detection: A Study with Diffusion Modelscited by 36
  18. 2024X2-DFD: A framework for eXplainable and eXtendable Deepfake Detectioncited by 35
  19. 2025D3: Training-Free AI-Generated Video Detection Using Second-Order Featurescited by 32
  20. 2025BusterX: MLLM-Powered AI-Generated Video Forgery Detection and Explanationcited by 30
  21. 2025BusterX++: Towards Unified Cross-Modal AI-Generated Content Detection and Explanation with MLLMcited by 28
  22. 2024MoE-FFD: Mixture of Experts for Generalized and Parameter-Efficient Face Forgery Detectioncited by 27
  23. 2025Exploring Unbiased Deepfake Detection via Token-Level Shuffling and Mixingcited by 27
  24. 2023Locate and Verify: A Two-Stream Network for Improved Deepfake Detectioncited by 26
  25. 2024Learning Natural Consistency Representation for Face Forgery Video Detectioncited by 26
  26. 2022MINTIME: Multi-Identity Size-Invariant Video Deepfake Detectioncited by 23
  27. 2024Circumventing shortcuts in audio-visual deepfake detection datasets with unsupervised learningcited by 23
  28. 2024Learning Spatiotemporal Inconsistency via Thumbnail Layout for Face Deepfake Detectioncited by 21
  29. 2025Deepfake Detection that Generalizes Across Benchmarkscited by 20
  30. 2022Digital and Physical Face Attacks: Reviewing and One Step Furthercited by 17
  31. 2023Detecting Deepfakes Without Seeing Anycited by 17
  32. 2025Zooming In on Fakes: A Novel Dataset for Localized AI-Generated Image Detection with Forgery Amplification Approachcited by 16
  33. 2024Exposing Lip-syncing Deepfakes from Mouth Inconsistenciescited by 15
  34. 2024SoK: Systematization and Benchmarking of Deepfake Detectors in a Unified Frameworkcited by 14
  35. 2025From Specificity to Generality: Revisiting Generalizable Artifacts in Detecting Face Deepfakescited by 14
  36. 2022Analyzing Fairness in Deepfake Detection With Massively Annotated Databasescited by 13
  37. 2023Detecting Images Generated by Diffuserscited by 12
  38. 2024Delocate: Detection and Localization for Deepfake Videos with Randomly-Located Tampered Tracescited by 12

50 most cited of 158 All 158 (JSON)