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Cited by 67Digital Media Forensic DetectionImage and Video Quality AssessmentGenerative Adversarial Networks and Image Synthesis

DeepFake Detection Based on High-Frequency Enhancement Network for Highly Compressed Content

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Gao, Jie, Xia, Zhaoqiang, Marcialis, Gian Luca, Dang, Chen, Dai, Jing, Feng, Xiaoyi·2024·Expert Systems with Applications
DOI 10.1016/j.eswa.2024.123732
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  • Gao et al. (2024) proposed a High-Frequency Enhancement (HiFE) network that leverages DCT and DWT to recover high-frequency details lost during compression, significantly improving detection performance on highly compressed deepfake content.

    “For instance, Gao et al. (2024) proposed a High-Frequency Enhancement (HiFE) network that leverages the Discrete Cosine Transform (DCT) and the Discrete Wavelet Transform (DWT) to recover high-frequency details adaptively lost during compression, significantly improving detection performance on highly compressed deepfake content [22].”

    From Deepfake Media Forensics: Status and Future Challenges (2025) · cites this paper as [22]

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References 79 · Cited by 67

Research works printed but not linked: 18

  1. [1]1. Darius Afchar, Vincent Nozick, Junichi Yamagishi, and Isao Echizen. Mesonet: a compact facial video forgery detection network. In Proceedings of the International Workshop on Information Forensics and Security (WIFS), pages 1-7. IEEE, 2018.
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  8. [32]32. Y Li, X Yang, P Sun, H Qi, and S Celeb-DF Lyu. A large-scale challenging dataset for deepfake forensics. In Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, pages 14-19. IEEE, 2020.
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  11. [43]43. Falko Matern, Christian Riess, and Marc Stamminger. Exploiting visual artifacts to expose deepfakes and face manipulations. In Proceedings of the Winter Applications of Computer Vision Workshops (WACVW), pages 83-92. IEEE, 2019.
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  13. [54]54. Andreas Rossler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, and Matthias Nießner. Faceforensics++: Learning to detect manipulated facial images. In Proceedings of the International Conference on Computer Vision (ICCV), pages 1-11. IEEE, 2019.
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Authors
Gao, J., Xia, Z., Marcialis, G. L., Dang, C., Dai, J., & Feng, X.
Affiliations
  • Northwestern Polytechnical University (CN)
  • University of Cagliari (IT)
  • China Academy of Launch Vehicle Technology (CN)
Corresponding author
Feng, Xiaoyi
ORCID
Venue
Expert Systems with Applications
Publisher
Elsevier BV
Published
2024-09
Volume
249
Pages
123732
ISSN
0957-4174 (ISSN-L)
DOI
10.1016/j.eswa.2024.123732
OpenAlex
W4392948149
Cited by
67
References
79
Citations by year
2026: 32 · 2025: 26 · 2024: 9
FWCI
9.38
Type
journal article
Language
en
Open access
Licence
Locations
  • Expert Systems with Applications (publishedVersion)
  • UNICA IRIS Institutional Research Information System (University of Cagliari) (submittedVersion, cc-by-nc-nd)
Funders
China Scholarship Council (202206290093)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
Keywords
deepfake detection, high-frequency enhancement, discrete cosine transform, discrete wavelet transform, channel attention, multi-scale feature fusion, face forensics, synthetic video detection

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