mlsanityData to 7 Oct 2026

FaceForensics++: Learning to Detect Manipulated Facial Images

arXiv 1901.08971 ↗First posted 25 Jan 2019cs.CVcited by 1,095

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Cited by 60 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

The rapid progress in synthetic image generation and manipulation has now come to a point where it raises significant concerns for the implications towards society. At best, this leads to a loss of trust in digital content, but could potentially cause further harm by spreading false information or fake news. This paper examines the realism of state-of-the-art image manipulations, and how difficult it is to detect them, either automatically or by humans. To standardize the evaluation of detection methods, we propose an automated benchmark for facial manipulation detection. In particular, the benchmark is based on DeepFakes, Face2Face, FaceSwap and NeuralTextures as prominent representatives for facial manipulations at random compression level and size. The benchmark is publicly available and contains a hidden test set as well as a database of over 1.8 million manipulated images. This dataset is over an order of magnitude larger than comparable, publicly available, forgery datasets. Based on this data, we performed a thorough analysis of data-driven forgery detectors. We show that the use of additional domainspecific knowledge improves forgery detection to unprecedented accuracy, even in the presence of strong compression, and clearly outperforms human observers.

Key facts. Cited by 1,095 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Jan 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 137; 2022: 148; 2023: 163; 2024: 194; 2025: 245; 2026: 208.

1,095 papers.

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  1. 2023Towards Universal Fake Image Detectors that Generalize Across Generative Modelscited by 483
  2. 2021Advances in Neural Renderingcited by 365
  3. 2021Multi-attentional Deepfake Detectioncited by 339
  4. 2022Detecting Deepfakes with Self-Blended Imagescited by 332
  5. 2024OpenVid-1M: A Large-Scale High-Quality Dataset for Text-to-video Generationcited by 332
  6. 2021Generalizing Face Forgery Detection with High-frequency Featurescited by 261
  7. 2021SimSwap: An Efficient Framework For High Fidelity Face Swappingcited by 243
  8. 2021Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domaincited by 241
  9. 2021Neural Head Avatars from Monocular RGB Videoscited by 212
  10. 2021WildDeepfake: A Challenging Real-World Dataset for Deepfake Detectioncited by 207
  11. 2021Trustworthy AI: From Principles to Practicescited by 206
  12. 2021High-Fidelity GAN Inversion for Image Attribute Editingcited by 197
38 more
  1. 2023Forgery-aware Adaptive Transformer for Generalizable Synthetic Image Detectioncited by 180
  2. 2021FakeAVCeleb: A Novel Audio-Video Multimodal Deepfake Datasetcited by 179
  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. 2023DeepfakeBench: A Comprehensive Benchmark of Deepfake Detectioncited by 162
  6. 2023Raising the Cost of Malicious AI-Powered Image Editingcited by 160
  7. 2021Exploring Temporal Coherence for More General Video Face Forgery Detectioncited by 158
  8. 2022Instant Volumetric Head Avatarscited by 157
  9. 2022EAMM: One-Shot Emotional Talking Face via Audio-Based Emotion-Aware Motion Modelcited by 151
  10. 2023Hierarchical Fine-Grained Image Forgery Detection and Localizationcited by 134
  11. 2021Frequency-aware Discriminative Feature Learning Supervised by Single-Center Loss for Face Forgery Detectioncited by 133
  12. 2022Leveraging Real Talking Faces via Self-Supervision for Robust Forgery Detectioncited by 130
  13. 2023State of the Art on Diffusion Models for Visual Computingcited by 126
  14. 2023Transcending Forgery Specificity with Latent Space Augmentation for Generalizable Deepfake Detectioncited by 126
  15. 2024DF40: Toward Next-Generation Deepfake Detectioncited by 126
  16. 2021ForgeryNet: A Versatile Benchmark for Comprehensive Forgery Analysiscited by 124
  17. 2024AEROBLADE: Training-Free Detection of Latent Diffusion Images Using Autoencoder Reconstruction Errorcited by 123
  18. 2022Implicit Identity Leakage: The Stumbling Block to Improving Deepfake Detection Generalizationcited by 122
  19. 2022Semantic-Aware Implicit Neural Audio-Driven Video Portrait Generationcited by 119
  20. 2023PatchCraft: Exploring Texture Patch for Efficient AI-generated Image Detectioncited by 119
  21. 2021M2TR: Multi-modal Multi-scale Transformers for Deepfake Detectioncited by 114
  22. 2024C2P-CLIP: Injecting Category Common Prompt in CLIP to Enhance Generalization in Deepfake Detectioncited by 111
  23. 2021Representative Forgery Mining for Fake Face Detectioncited by 108
  24. 2021One Shot Face Swapping on Megapixelscited by 105
  25. 2024Orthogonal Subspace Decomposition for Generalizable AI-Generated Image Detectioncited by 102
  26. 2021Local Relation Learning for Face Forgery Detectioncited by 100
  27. 2021Dual Contrastive Learning for General Face Forgery Detectioncited by 100
  28. 2023TALL: Thumbnail Layout for Deepfake Video Detectioncited by 100
  29. 2023Detecting and Grounding Multi-Modal Media Manipulationcited by 98
  30. 2022Protecting Celebrities from DeepFake with Identity Consistency Transformercited by 95
  31. 2021HifiFace: 3D Shape and Semantic Prior Guided High Fidelity Face Swappingcited by 92
  32. 2021Spatiotemporal Inconsistency Learning for DeepFake Video Detectioncited by 88
  33. 2022Fusing Global and Local Features for Generalized AI-Synthesized Image Detectioncited by 88
  34. 2024LAA-Net: Localized Artifact Attention Network for Quality-Agnostic and Generalizable Deepfake Detectioncited by 88
  35. 2024Leveraging Representations from Intermediate Encoder-blocks for Synthetic Image Detectioncited by 88
  36. 2021Face Forensics in the Wildcited by 87
  37. 2023Self-Supervised Video Forensics by Audio-Visual Anomaly Detectioncited by 84
  38. 2021Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forwardcited by 82

50 most cited of 1,095 All 1,095 (JSON)