mlsanityData to 7 Oct 2026

Learning Self-Consistency for Deepfake Detection

arXiv 2012.09311 ↗First posted 16 Dec 2020cs.CVcited by 179

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Abstract (arXiv)

We propose a new method to detect deepfake images using the cue of the source feature inconsistency within the forged images. It is based on the hypothesis that images' distinct source features can be preserved and extracted after going through state-of-the-art deepfake generation processes. We introduce a novel representation learning approach, called pair-wise self-consistency learning (PCL), for training ConvNets to extract these source features and detect deepfake images. It is accompanied by a new image synthesis approach, called inconsistency image generator (I2G), to provide richly annotated training data for PCL. Experimental results on seven popular datasets show that our models improve averaged AUC over the state of the art from 96.45% to 98.05% in the in-dataset evaluation and from 86.03% to 92.18% in the cross-dataset evaluation.

Key facts. Cited by 179 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Dec 2020.

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Citing papers per year: 2021: 6; 2022: 31; 2023: 44; 2024: 45; 2025: 39; 2026: 14.

179 papers.

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  1. 2022Detecting Deepfakes with Self-Blended Imagescited by 332
  2. 2023Forgery-aware Adaptive Transformer for Generalizable Synthetic Image Detectioncited by 180
  3. 2023UCF: Uncovering Common Features for Generalizable Deepfake Detectioncited by 177
  4. 2022Self-supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detectioncited by 172
  5. 2023Hierarchical Fine-Grained Image Forgery Detection and Localizationcited by 134
  6. 2022Leveraging Real Talking Faces via Self-Supervision for Robust Forgery Detectioncited by 130
  7. 2023Transcending Forgery Specificity with Latent Space Augmentation for Generalizable Deepfake Detectioncited by 126
  8. 2024DF40: Toward Next-Generation Deepfake Detectioncited by 126
  9. 2022Implicit Identity Leakage: The Stumbling Block to Improving Deepfake Detection Generalizationcited by 122
  10. 2024Orthogonal Subspace Decomposition for Generalizable AI-Generated Image Detectioncited by 102
  11. 2023Detecting and Grounding Multi-Modal Media Manipulationcited by 98
  12. 2024LAA-Net: Localized Artifact Attention Network for Quality-Agnostic and Generalizable Deepfake Detectioncited by 88
38 more
  1. 2023Self-Supervised Video Forensics by Audio-Visual Anomaly Detectioncited by 84
  2. 2021Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forwardcited by 82
  3. 2023AltFreezing for More General Video Face Forgery Detectioncited by 78
  4. 2022CORE: Consistent Representation Learning for Face Forgery Detectioncited by 73
  5. 2022UIA-ViT: Unsupervised Inconsistency-Aware Method based on Vision Transformer for Face Forgery Detectioncited by 69
  6. 2023BlendFace: Re-designing Identity Encoders for Face-Swappingcited by 66
  7. 2024Deepfake Generation and Detection: A Benchmark and Surveycited by 66
  8. 2025Spot the Fake: Large Multimodal Model-Based Synthetic Image Detection with Artifact Explanationcited by 66
  9. 2024Generalizing Deepfake Video Detection with Plug-and-Play: Video-Level Blending and Spatiotemporal Adapter Tuningcited by 57
  10. 2023Detecting and Grounding Multi-Modal Media Manipulation and Beyondcited by 55
  11. 2023Beyond the Prior Forgery Knowledge: Mining Critical Clues for General Face Forgery Detectioncited by 50
  12. 2022SeeABLE: Soft Discrepancies and Bounded Contrastive Learning for Exposing Deepfakescited by 49
  13. 2022Deepfake Detection: A Comprehensive Survey from the Reliability Perspectivecited by 48
  14. 2024FreqBlender: Enhancing DeepFake Detection by Blending Frequency Knowledgecited by 48
  15. 2024Can We Leave Deepfake Data Behind in Training Deepfake Detector?cited by 48
  16. 2024Exposing the Deception: Uncovering More Forgery Clues for Deepfake Detectioncited by 47
  17. 2023Towards General Visual-Linguistic Face Forgery Detectioncited by 45
  18. 2022Audio-Visual Person-of-Interest DeepFake Detectioncited by 43
  19. 2023SepMark: Deep Separable Watermarking for Unified Source Tracing and Deepfake Detectioncited by 43
  20. 2022Delving into Sequential Patches for Deepfake Detectioncited by 41
  21. 2022FDNeRF: Few-shot Dynamic Neural Radiance Fields for Face Reconstruction and Expression Editingcited by 38
  22. 2024FKA-Owl: Advancing Multimodal Fake News Detection through Knowledge-Augmented LVLMscited by 38
  23. 2025Rethinking Vision-Language Model in Face Forensics: Multi-Modal Interpretable Forged Face Detectorcited by 38
  24. 2024Towards More General Video-based Deepfake Detection through Facial Component Guided Adaptation for Foundation Modelcited by 37
  25. 2022Learning Second Order Local Anomaly for General Face Forgery Detectioncited by 36
  26. 2024X2-DFD: A framework for eXplainable and eXtendable Deepfake Detectioncited by 35
  27. 2022Detecting and Recovering Sequential DeepFake Manipulationcited by 34
  28. 2022Explaining Deepfake Detection by Analysing Image Matchingcited by 34
  29. 2024Fake It till You Make It: Curricular Dynamic Forgery Augmentations towards General Deepfake Detectioncited by 30
  30. 2024DiffusionFake: Enhancing Generalization in Deepfake Detection via Guided Stable Diffusioncited by 29
  31. 2022Synthetic Data in Human Analysis: A Surveycited by 28
  32. 2023Contrastive Pseudo Learning for Open-World DeepFake Attributioncited by 27
  33. 2025Exploring Unbiased Deepfake Detection via Token-Level Shuffling and Mixingcited by 27
  34. 2023DeepFake-Adapter: Dual-Level Adapter for DeepFake Detectioncited by 26
  35. 2023Locate and Verify: A Two-Stream Network for Improved Deepfake Detectioncited by 26
  36. 2021Wavelet-Packets for Deepfake Image Analysis and Detectioncited by 25
  37. 2022Discovering Transferable Forensic Features for CNN-generated Images Detectioncited by 24
  38. 2023Continual Face Forgery Detection via Historical Distribution Preservingcited by 23

50 most cited of 179 All 179 (JSON)