mlsanityData to 7 Oct 2026

Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain

arXiv 2103.01856 ↗First posted 2 Mar 2021cs.CVcited by 241

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Abstract (arXiv)

The remarkable success in face forgery techniques has received considerable attention in computer vision due to security concerns. We observe that up-sampling is a necessary step of most face forgery techniques, and cumulative up-sampling will result in obvious changes in the frequency domain, especially in the phase spectrum. According to the property of natural images, the phase spectrum preserves abundant frequency components that provide extra information and complement the loss of the amplitude spectrum. To this end, we present a novel Spatial-Phase Shallow Learning (SPSL) method, which combines spatial image and phase spectrum to capture the up-sampling artifacts of face forgery to improve the transferability, for face forgery detection. And we also theoretically analyze the validity of utilizing the phase spectrum. Moreover, we notice that local texture information is more crucial than high-level semantic information for the face forgery detection task. So we reduce the receptive fields by shallowing the network to suppress high-level features and focus on the local region. Extensive experiments show that SPSL can achieve the state-of-the-art performance on cross-datasets evaluation as well as multi-class classification and obtain comparable results on single dataset evaluation.

Key facts. Cited by 241 papers in the map (data through 7 Oct 2026). Builds on 35 of its 53 references that are in the map. First posted on arXiv 2 Mar 2021.

Builds on

35 of 53 references.

References · 35

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  8. 2017Dynamic Routing Between Capsulescited by 689
  9. 2020Face2Face: Real-time Face Capture and Reenactment of RGB Videoscited by 631
  10. 2019Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensicscited by 573
  11. 2020Thinking in Frequency: Face Forgery Detection by Mining Frequency-aware Cluescited by 518
  12. 2019Face X-ray for More General Face Forgery Detectioncited by 469
23 more
  1. 2018MesoNet: a Compact Facial Video Forgery Detection Networkcited by 409
  2. 2019FSGAN: Subject Agnostic Face Swapping and Reenactmentcited by 319
  3. 2018Exposing DeepFake Videos By Detecting Face Warping Artifactscited by 318
  4. 2019High Frequency Component Helps Explain the Generalization of Convolutional Neural Networkscited by 310
  5. 2018Exposing Deep Fakes Using Inconsistent Head Posescited by 294
  6. 2020Watch your Up-Convolution: CNN Based Generative Deep Neural Networks are Failing to Reproduce Spectral Distributionscited by 278
  7. 2020What makes fake images detectable? Understanding properties that generalizecited by 272
  8. 2019On the Detection of Digital Face Manipulationcited by 259
  9. 2019Deferred Neural Rendering: Image Synthesis using Neural Texturescited by 253
  10. 2018DeepFakes: a New Threat to Face Recognition? Assessment and Detectioncited by 208
  11. 2018Capsule-Forensics: Using Capsule Networks to Detect Forged Images and Videoscited by 180
  12. 2020Two-branch Recurrent Network for Isolating Deepfakes in Videoscited by 161
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  14. 2019Multi-task Learning For Detecting and Segmenting Manipulated Facial Images and Videoscited by 130
  15. 2018Two-Stream Neural Networks for Tampered Face Detectioncited by 125
  16. 2020The DeepFake Detection Challenge (DFDC) Datasetcited by 125
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  18. 2019Recurrent Convolutional Strategies for Face Manipulation Detection in Videoscited by 118
  19. 2019Unmasking DeepFakes with simple Featurescited by 78
  20. 2017Recasting Residual-based Local Descriptors as Convolutional Neural Networks: an Application to Image Forgery Detectioncited by 56
  21. 2020Sharp Multiple Instance Learning for DeepFake Video Detectioncited by 54
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  23. 2019Towards Generalizable Deepfake Detection with Locality-aware AutoEncodercited by 17

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 11; 2022: 37; 2023: 51; 2024: 47; 2025: 57; 2026: 38.

241 papers.

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  1. 2021Generalized Out-of-Distribution Detection: A Surveycited by 796
  2. 2022Detecting Deepfakes with Self-Blended Imagescited by 332
  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. 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. 2023PatchCraft: Exploring Texture Patch for Efficient AI-generated Image Detectioncited by 119
  11. 2024Orthogonal Subspace Decomposition for Generalizable AI-Generated Image Detectioncited by 102
  12. 2021Dual Contrastive Learning for General Face Forgery Detectioncited by 100
38 more
  1. 2023TALL: Thumbnail Layout for Deepfake Video Detectioncited by 100
  2. 2023Detecting and Grounding Multi-Modal Media Manipulationcited by 98
  3. 2022CORE: Consistent Representation Learning for Face Forgery Detectioncited by 73
  4. 2021Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency Domaincited by 70
  5. 2021Reverse Engineering of Generative Models: Inferring Model Hyperparameters from Generated Imagescited by 58
  6. 2025Dual Data Alignment Makes AI-Generated Image Detector Easier Generalizablecited by 58
  7. 2024Generalizing Deepfake Video Detection with Plug-and-Play: Video-Level Blending and Spatiotemporal Adapter Tuningcited by 57
  8. 2023Detecting and Grounding Multi-Modal Media Manipulation and Beyondcited by 55
  9. 2023Beyond the Prior Forgery Knowledge: Mining Critical Clues for General Face Forgery Detectioncited by 50
  10. 2022SeeABLE: Soft Discrepancies and Bounded Contrastive Learning for Exposing Deepfakescited by 49
  11. 2022Deepfake Detection: A Comprehensive Survey from the Reliability Perspectivecited by 48
  12. 2024Can We Leave Deepfake Data Behind in Training Deepfake Detector?cited by 48
  13. 2022Deepfake Network Architecture Attributioncited by 47
  14. 2024Exposing the Deception: Uncovering More Forgery Clues for Deepfake Detectioncited by 47
  15. 2022Exploring Frequency Adversarial Attacks for Face Forgery Detectioncited by 45
  16. 2023Towards General Visual-Linguistic Face Forgery Detectioncited by 45
  17. 2022Voice-Face Homogeneity Tells Deepfakecited by 44
  18. 2022Audio-Visual Person-of-Interest DeepFake Detectioncited by 43
  19. 2023Perceptual Artifacts Localization for Image Synthesis Taskscited by 43
  20. 2022Delving into Sequential Patches for Deepfake Detectioncited by 41
  21. 2021Detect and Locate: Exposing Face Manipulation by Semantic- and Noise-level Telltalescited by 39
  22. 2025Rethinking Vision-Language Model in Face Forensics: Multi-Modal Interpretable Forged Face Detectorcited by 38
  23. 2022Exploring Disentangled Content Information for Face Forgery Detectioncited by 36
  24. 2022Learning Second Order Local Anomaly for General Face Forgery Detectioncited by 36
  25. 2023Robustness and Generalizability of Deepfake Detection: A Study with Diffusion Modelscited 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. 2024Poisoned Forgery Face: Towards Backdoor Attacks on Face Forgery Detectioncited by 34
  30. 2024Lips Are Lying: Spotting the Temporal Inconsistency between Audio and Visual in Lip-Syncing DeepFakescited by 32
  31. 2024Standing on the Shoulders of Giants: Reprogramming Visual-Language Model for General Deepfake Detectioncited by 30
  32. 2024Fake It till You Make It: Curricular Dynamic Forgery Augmentations towards General Deepfake Detectioncited by 30
  33. 2024DiffusionFake: Enhancing Generalization in Deepfake Detection via Guided Stable Diffusioncited by 29
  34. 2024AI-Face: A Million-Scale Demographically Annotated AI-Generated Face Dataset and Fairness Benchmarkcited by 28
  35. 2024MoE-FFD: Mixture of Experts for Generalized and Parameter-Efficient Face Forgery Detectioncited by 27
  36. 2023DeepFake-Adapter: Dual-Level Adapter for DeepFake Detectioncited by 26
  37. 2023Locate and Verify: A Two-Stream Network for Improved Deepfake Detectioncited by 26
  38. 2022Benchmarking Joint Face Spoofing and Forgery Detection with Visual and Physiological Cuescited by 24

50 most cited of 241 All 241 (JSON)