mlsanityData to 7 Oct 2026

Frequency Masking for Universal Deepfake Detection

arXiv 2401.06506 ↗First posted 12 Jan 2024cs.CV cs.AIcited by 23

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Abstract (arXiv)

We study universal deepfake detection. Our goal is to detect synthetic images from a range of generative AI approaches, particularly from emerging ones which are unseen during training of the deepfake detector. Universal deepfake detection requires outstanding generalization capability. Motivated by recently proposed masked image modeling which has demonstrated excellent generalization in self-supervised pre-training, we make the first attempt to explore masked image modeling for universal deepfake detection. We study spatial and frequency domain masking in training deepfake detectors. Based on empirical analysis, we propose a novel deepfake detector via frequency masking. Our focus on frequency domain is different from the majority, which primarily target spatial domain detection. Our comparative analyses reveal substantial performance gains over existing methods. Code and models are publicly available.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). Builds on 13 of its 17 references that are in the map. First posted on arXiv 12 Jan 2024.

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13 of 17 references.

References · 13

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  3. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,339
  4. 2019CNN-generated images are surprisingly easy to spot... for nowcited by 842
  5. 2023Towards Universal Fake Image Detectors that Generalize Across Generative Modelscited by 483
  6. 2022On the detection of synthetic images generated by diffusion modelscited by 282
  7. 2020What makes fake images detectable? Understanding properties that generalizecited by 272
  8. 2020The Creation and Detection of Deepfakes: A Surveycited by 246
  9. 2021Are GAN generated images easy to detect? A critical analysis of the state-of-the-artcited by 139
  10. 2023Intriguing properties of synthetic images: from generative adversarial networks to diffusion modelscited by 121
  11. 2022Masked Frequency Modeling for Self-Supervised Visual Pre-Trainingcited by 70
  12. 2022Discovering Transferable Forensic Features for CNN-generated Images Detectioncited by 24
1 more
  1. 2023A Survey on Generative Modeling with Limited Data, Few Shots, and Zero Shotcited by 19

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 8; 2025: 9; 2026: 6.

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  1. 2024VidProM: A Million-scale Real Prompt-Gallery Dataset for Text-to-Video Diffusion Modelscited by 124
  2. 2024Any-Resolution AI-Generated Image Detection by Spectral Learningcited by 56
  3. 2024FIRE: Robust Detection of Diffusion-Generated Images via Frequency-Guided Reconstruction Errorcited by 46
  4. 2025AI-Generated Video Detection via Perceptual Straighteningcited by 34
  5. 2024Vulnerabilities in AI-generated Image Detection: The Challenge of Adversarial Attackscited by 12
  6. 2025LOTA: Bit-Planes Guided AI-Generated Image Detectioncited by 10
  7. 2024Evolving from Single-modal to Multi-modal Facial Deepfake Detection: Progress and Challengescited by 6
  8. 2026Simplicity Prevails: The Emergence of Generalizable AIGI Detection in Visual Foundation Modelscited by 6
  9. 2025DATA: Multi-Disentanglement based Contrastive Learning for Open-World Semi-Supervised Deepfake Attributioncited by 5
  10. 2024DuoCast: Duo-Probabilistic Diffusion for Precipitation Nowcastingcited by 4
  11. 2025Fully AI-Generated Image Detection: Definition, Recent Advances and Challengescited by 4
  12. 2025Towards Reliable Identification of Diffusion-based Image Manipulationscited by 4
11 more
  1. 2026MPF-Net: Exposing High-Fidelity AI-Generated Video Forgeries via Hierarchical Manifold Deviation and Micro-Temporal Fluctuationscited by 2
  2. 2024The Visual Counter Turing Test (VCT2): A Benchmark for Evaluating AI-Generated Image Detection and the Visual AI Index (VAI)cited by 1
  3. 2025Knowledge-Guided Prompt Learning for Deepfake Facial Image Detectioncited by 1
  4. 2025FreqCross: A Multi-Modal Frequency-Spatial Fusion Network for Robust Detection of Stable Diffusion 3.5 Generated Imagescited by 1
  5. 2024Towards More Accurate Fake Detection on Images Generated from Advanced Generative and Neural Rendering Modelscited by 0
  6. 2025Unmasking Synthetic Realities in Generative AI: A Comprehensive Review of Adversarially Robust Deepfake Detection Systemscited by 0
  7. 2025Fair and Interpretable Deepfake Detection in Videoscited by 0
  8. 2026Detecting Diffusion-generated Images via Dynamic Assembly Forestscited by 0
  9. 2026An Explainable FFT-Based Spatial-Frequency Fusion Framework for Deepfake Detectioncited by 0
  10. 2026Mind the Rift: Cross-Scale Coupling Mismatch for AI-Generated Video Detectioncited by 0
  11. 2026From Detection to Localization: A Unified Forensics Framework for Fully Synthetic and Tampered Imagescited by 0