mlsanityData to 7 Oct 2026

On the Detection of Digital Face Manipulation

arXiv 1910.01717 ↗First posted 3 Oct 2019cs.CVcited by 259

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Abstract (arXiv)

Detecting manipulated facial images and videos is an increasingly important topic in digital media forensics. As advanced face synthesis and manipulation methods are made available, new types of fake face representations are being created which have raised significant concerns for their use in social media. Hence, it is crucial to detect manipulated face images and localize manipulated regions. Instead of simply using multi-task learning to simultaneously detect manipulated images and predict the manipulated mask (regions), we propose to utilize an attention mechanism to process and improve the feature maps for the classification task. The learned attention maps highlight the informative regions to further improve the binary classification (genuine face v. fake face), and also visualize the manipulated regions. To enable our study of manipulated face detection and localization, we collect a large-scale database that contains numerous types of facial forgeries. With this dataset, we perform a thorough analysis of data-driven fake face detection. We show that the use of an attention mechanism improves facial forgery detection and manipulated region localization.

Key facts. Cited by 259 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Oct 2019.

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Citing papers per year: 2021: 43; 2022: 45; 2023: 49; 2024: 42; 2025: 54; 2026: 26.

259 papers.

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  1. 2021Multi-attentional Deepfake Detectioncited by 339
  2. 2022Detecting Deepfakes with Self-Blended Imagescited by 332
  3. 2023GenImage: A Million-Scale Benchmark for Detecting AI-Generated Imagecited by 318
  4. 2021Generalizing Face Forgery Detection with High-frequency Featurescited by 261
  5. 2021Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domaincited by 241
  6. 2023UCF: Uncovering Common Features for Generalizable Deepfake Detectioncited by 177
  7. 2023DeepfakeBench: A Comprehensive Benchmark of Deepfake Detectioncited by 162
  8. 2023Hierarchical Fine-Grained Image Forgery Detection and Localizationcited by 134
  9. 2021Frequency-aware Discriminative Feature Learning Supervised by Single-Center Loss for Face Forgery Detectioncited by 133
  10. 2021PSCC-Net: Progressive Spatio-Channel Correlation Network for Image Manipulation Detection and Localizationcited by 133
  11. 2022Leveraging Real Talking Faces via Self-Supervision for Robust Forgery Detectioncited by 130
  12. 2023Transcending Forgery Specificity with Latent Space Augmentation for Generalizable Deepfake Detectioncited by 126
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  1. 2024DF40: Toward Next-Generation Deepfake Detectioncited by 126
  2. 2021ForgeryNet: A Versatile Benchmark for Comprehensive Forgery Analysiscited by 124
  3. 2024FakeShield: Explainable Image Forgery Detection and Localization via Multi-modal Large Language Modelscited by 111
  4. 2021Representative Forgery Mining for Fake Face Detectioncited by 108
  5. 2021Local Relation Learning for Face Forgery Detectioncited by 100
  6. 2021Dual Contrastive Learning for General Face Forgery Detectioncited by 100
  7. 2022Protecting Celebrities from DeepFake with Identity Consistency Transformercited by 95
  8. 2021Spatiotemporal Inconsistency Learning for DeepFake Video Detectioncited by 88
  9. 2021Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forwardcited by 82
  10. 2023AltFreezing for More General Video Face Forgery Detectioncited by 78
  11. 2022CORE: Consistent Representation Learning for Face Forgery Detectioncited by 73
  12. 2022UIA-ViT: Unsupervised Inconsistency-Aware Method based on Vision Transformer for Face Forgery Detectioncited by 69
  13. 2024Deepfake Generation and Detection: A Benchmark and Surveycited by 66
  14. 2023Seeing is not always believing: Benchmarking Human and Model Perception of AI-Generated Imagescited by 65
  15. 2021BiHPF: Bilateral High-Pass Filters for Robust Deepfake Detectioncited by 60
  16. 2021Exploiting Fine-grained Face Forgery Clues via Progressive Enhancement Learningcited by 60
  17. 2024FFAA: Multimodal Large Language Model based Explainable Open-World Face Forgery Analysis Assistantcited by 59
  18. 2021Reverse Engineering of Generative Models: Inferring Model Hyperparameters from Generated Imagescited by 58
  19. 2024Generalizing Deepfake Video Detection with Plug-and-Play: Video-Level Blending and Spatiotemporal Adapter Tuningcited by 57
  20. 2021Countering Malicious DeepFakes: Survey, Battleground, and Horizoncited by 56
  21. 2021Deep Gait Recognition: A Surveycited by 54
  22. 2023Multimodal Automated Fact-Checking: A Surveycited by 51
  23. 2022SeeABLE: Soft Discrepancies and Bounded Contrastive Learning for Exposing Deepfakescited by 49
  24. 2022Deepfake Detection: A Comprehensive Survey from the Reliability Perspectivecited by 48
  25. 2024Can We Leave Deepfake Data Behind in Training Deepfake Detector?cited by 48
  26. 2023AutoSplice: A Text-prompt Manipulated Image Dataset for Media Forensicscited by 46
  27. 2022Exploring Frequency Adversarial Attacks for Face Forgery Detectioncited by 45
  28. 2023Towards General Visual-Linguistic Face Forgery Detectioncited by 45
  29. 2022Audio-Visual Person-of-Interest DeepFake Detectioncited by 43
  30. 2022Delving into Sequential Patches for Deepfake Detectioncited by 41
  31. 2025Deepfake-Eval-2024: A Multi-Modal In-the-Wild Benchmark of Deepfakes Circulated in 2024cited by 40
  32. 2021Detect and Locate: Exposing Face Manipulation by Semantic- and Noise-level Telltalescited by 39
  33. 2022Multi-domain Learning for Updating Face Anti-spoofing Modelscited by 39
  34. 2025Rethinking Vision-Language Model in Face Forensics: Multi-Modal Interpretable Forged Face Detectorcited by 38
  35. 2021Initiative Defense against Facial Manipulationcited by 36
  36. 2022Exploring Disentangled Content Information for Face Forgery Detectioncited by 36
  37. 2022Learning Second Order Local Anomaly for General Face Forgery Detectioncited by 36
  38. 2023Robustness and Generalizability of Deepfake Detection: A Study with Diffusion Modelscited by 36

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