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Global Texture Enhancement for Fake Face Detection in the Wild
Open accessAbstract
Generative Adversarial Networks (GANs) can generate realistic fake face images that can easily fool human beings. On the contrary, a common Convolutional Neural Network(CNN) discriminator can achieve more than99.9%accuracyin discerning fake/real images. In this paper, we conduct an empirical study on fake/real faces, and have two important observations: firstly, the texture of fake faces is substantially different from real ones; secondly, global texture statistics are more robust to image editing and transferable to fake faces from different GANs and datasets. Motivated by the above observations, we propose a new architecture coined as Gram-Net, which leverages global image texture representations for robust fake image detection. Experimental results on several datasets demonstrate that our Gram-Netoutperforms existing approaches. Especially, our Gram-Netis more robust to image editings, e.g. down-sampling, JPEGcompression, blur, and noise. More importantly, our Gram-Net generalizes significantly better in detecting fake faces from GAN models not seen in the training phase and can perform decently in detecting fake natural images.
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“Common visual artifacts include lighting and color mismatches [51], unusual facial textures [52], irregular frequency patterns [56], and inconsistent edge blending [59].”
From Evolving from Single-modal to Multi-modal Facial Deepfake Detection: Progress and Challenges (2024) · cites this paper as [52]
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“Several studies have shown that the frequency spectrum of GAN-generated images often contains distinctive patterns - 'checkerboards', particularly in the middle and high-frequency bands [85-89].”
From Deepfake detection across image, video, and audio: a comprehensive survey with empirical evaluation of generalization and robustness (2026) · cites this paper as [88]
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Metadata
- Authors
- Liu, Z., Qi, X., & Torr, P. H. S.
- Affiliations
- University of Oxford (GB)
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 8057-8066
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3034795015
- Cited by
- 401
- References
- 43
- Citations by year
- 2026: 55 · 2025: 102 · 2024: 71 · 2023: 64 · 2022: 57 · 2021: 42 · 2020: 9
- FWCI
- 13.26
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- The HKU Scholars Hub (University of Hong Kong) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Royal Academy of Engineering
- Multidisciplinary University Research Initiative (EP/N019474/1)
- Engineering and Physical Sciences Research Council (EP/N019474/1, EP/M013774/1, EP/M013774/, ERC-2012-AdG 321162-HELIOS, Seebibyte EP/M013774/1)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- face forgery detection, deepfake detection, GAN-generated images
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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