mlsanityData to 7 Oct 2026

MesoNet: a Compact Facial Video Forgery Detection Network

arXiv 1809.00888 ↗First posted 4 Sep 2018cs.CV eess.IVcited by 409

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Abstract (arXiv)

This paper presents a method to automatically and efficiently detect face tampering in videos, and particularly focuses on two recent techniques used to generate hyper-realistic forged videos: Deepfake and Face2Face. Traditional image forensics techniques are usually not well suited to videos due to the compression that strongly degrades the data. Thus, this paper follows a deep learning approach and presents two networks, both with a low number of layers to focus on the mesoscopic properties of images. We evaluate those fast networks on both an existing dataset and a dataset we have constituted from online videos. The tests demonstrate a very successful detection rate with more than 98% for Deepfake and 95% for Face2Face.

Key facts. Cited by 409 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Sep 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 76; 2022: 77; 2023: 75; 2024: 67; 2025: 69; 2026: 45.

409 papers.

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  1. 2021Multi-attentional Deepfake Detectioncited by 339
  2. 2022Detecting Deepfakes with Self-Blended Imagescited by 332
  3. 2021Generalizing Face Forgery Detection with High-frequency Featurescited by 261
  4. 2021Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domaincited by 241
  5. 2021WildDeepfake: A Challenging Real-World Dataset for Deepfake Detectioncited by 207
  6. 2021FakeAVCeleb: A Novel Audio-Video Multimodal Deepfake Datasetcited by 179
  7. 2023UCF: Uncovering Common Features for Generalizable Deepfake Detectioncited by 177
  8. 2022Self-supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detectioncited by 172
  9. 2023DeepfakeBench: A Comprehensive Benchmark of Deepfake Detectioncited by 162
  10. 2023Raising the Cost of Malicious AI-Powered Image Editingcited by 160
  11. 2021Exploring Temporal Coherence for More General Video Face Forgery Detectioncited by 158
  12. 2021Frequency-aware Discriminative Feature Learning Supervised by Single-Center Loss for Face Forgery Detectioncited by 133
38 more
  1. 2022Leveraging Real Talking Faces via Self-Supervision for Robust Forgery Detectioncited by 130
  2. 2022Does Audio Deepfake Detection Generalize?cited by 129
  3. 2023Transcending Forgery Specificity with Latent Space Augmentation for Generalizable Deepfake Detectioncited by 126
  4. 2024DF40: Toward Next-Generation Deepfake Detectioncited by 126
  5. 2022Implicit Identity Leakage: The Stumbling Block to Improving Deepfake Detection Generalizationcited by 122
  6. 2021M2TR: Multi-modal Multi-scale Transformers for Deepfake Detectioncited by 114
  7. 2021Local Relation Learning for Face Forgery Detectioncited by 100
  8. 2021Dual Contrastive Learning for General Face Forgery Detectioncited by 100
  9. 2023TALL: Thumbnail Layout for Deepfake Video Detectioncited by 100
  10. 2022Protecting Celebrities from DeepFake with Identity Consistency Transformercited by 95
  11. 2024LAA-Net: Localized Artifact Attention Network for Quality-Agnostic and Generalizable Deepfake Detectioncited by 88
  12. 2021Face Forensics in the Wildcited by 87
  13. 2021Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forwardcited by 82
  14. 2021Deepfake Video Detection Using Convolutional Vision Transformercited by 79
  15. 2023AV-Deepfake1M: A Large-Scale LLM-Driven Audio-Visual Deepfake Datasetcited by 79
  16. 2023AltFreezing for More General Video Face Forgery Detectioncited by 78
  17. 2022CORE: Consistent Representation Learning for Face Forgery Detectioncited by 73
  18. 2023BlendFace: Re-designing Identity Encoders for Face-Swappingcited by 66
  19. 2021Predicting Video with VQVAEcited by 61
  20. 2021Exploiting Fine-grained Face Forgery Clues via Progressive Enhancement Learningcited by 60
  21. 2021Reverse Engineering of Generative Models: Inferring Model Hyperparameters from Generated Imagescited by 58
  22. 2021Improving the Efficiency and Robustness of Deepfakes Detection through Precise Geometric Featurescited by 57
  23. 2021Countering Malicious DeepFakes: Survey, Battleground, and Horizoncited by 56
  24. 2022MARLIN: Masked Autoencoder for facial video Representation LearnINgcited by 55
  25. 2021CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakescited by 54
  26. 2021OpenForensics: Large-Scale Challenging Dataset For Multi-Face Forgery Detection And Segmentation In-The-Wildcited by 50
  27. 2023Beyond the Prior Forgery Knowledge: Mining Critical Clues for General Face Forgery Detectioncited by 50
  28. 2022SeeABLE: Soft Discrepancies and Bounded Contrastive Learning for Exposing Deepfakescited by 49
  29. 2022Deepfake Detection: A Comprehensive Survey from the Reliability Perspectivecited by 48
  30. 2024Can We Leave Deepfake Data Behind in Training Deepfake Detector?cited by 48
  31. 2022Exploring Frequency Adversarial Attacks for Face Forgery Detectioncited by 45
  32. 2022A Continual Deepfake Detection Benchmark: Dataset, Methods, and Essentialscited by 45
  33. 2023Towards General Visual-Linguistic Face Forgery Detectioncited by 45
  34. 2022Voice-Face Homogeneity Tells Deepfakecited by 44
  35. 2021FReTAL: Generalizing Deepfake Detection using Knowledge Distillation and Representation Learningcited by 43
  36. 2023Perceptual Artifacts Localization for Image Synthesis Taskscited by 43
  37. 2023Glitch in the Matrix: A Large Scale Benchmark for Content Driven Audio-Visual Forgery Detection and Localizationcited by 42
  38. 2022Delving into Sequential Patches for Deepfake Detectioncited by 41

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