CoChat
Sign Up
Cited by 106Digital Media Forensic DetectionGenerative Adversarial Networks and Image SynthesisImage and Video Quality Assessment

BiHPF: Bilateral High-Pass Filters for Robust Deepfake Detection

Open access
Jeong, Yonghyun, Kim, Doyeon, Min, Seungjai, Joe, Seongho, Gwon, Youngjune, Choi, Jongwon·2022·2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
DOI 10.1109/wacv51458.2022.00293
Open this paper in CoChat — free accountOpen PDF

Abstract

The advancement in numerous generative models has a two-fold effect: a simple and easy generation of realistic synthesized images, but also an increased risk of malicious abuse of those images. Thus, it is important to develop a generalized detector for synthesized images of any GAN model or object category, including those unseen during the training phase. However, the conventional methods heavily depend on the training settings, which cause a dramatic decline in performance when tested with unknown domains. To resolve the issue and obtain a generalized detection ability, we propose Bilateral High-Pass Filters (BiHPF), which amplify the effect of the frequency-level artifacts that are generally found in the synthesized images of generative models. Also, to find the properties of the general frequency-level artifacts, we develop an additional method to adversarially extract the artifact compression map. Numerous experimental results validate that our method outperforms other state-of-the-art methods, even when tested with unseen domains.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/wacv51458.2022.00293

Cites

9 of 9 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 23

  1. [1][1] Shruti Agarwal, Hany Farid, Yuming Gu, Mingming He, Koki Nagano, and Hao Li. Protecting world leaders against deep fakes. In CVPR Workshops , 2019. -
  2. [7][7] Davide Cozzolino, Justus Thies, Andreas R¨ ossler, Christian Riess, Matthias Nießner, and Luisa Verdoliva. Forensictransfer: Weakly-supervised domain adaptation for forgery detection. arXiv , 2018. -
  3. [8][8] Hao Dang, Feng Liu, Joel Stehouwer, Xiaoming Liu, and Anil K Jain. On the detection of digital face manipulation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 5781-5790, 2020. -
  4. [11][11] Ricard Durall, Margret Keuper, and Janis Keuper. Watch your Up-Convolution: CNN Based Generative Deep Neural Networks are Failing to Reproduce Spectral Distributions. In CVPR , 2020. -
  5. [12][12] Ricard Durall, Margret Keuper, Franz-Josef Pfreundt, and Janis Keuper. Unmasking deepfakes with simple features. arXiv , 2019. -
  6. [13][13] Pasquale Ferrara, Tiziano Bianchi, Alessia De Rosa, and Alessandro Piva. Image forgery localization via fine-grained analysis of cfa artifacts. IEEE Trans. Inf. Forensics Secur , 2012. -
  7. [14][14] Joel Frank, Thorsten Eisenhofer, Lea Sch¨ onherr, Asja Fischer, Dorothea Kolossa, and Thorsten Holz. Leveraging frequency analysis for deep fake image recognition. arXiv , 2020. -
  8. [16][16] Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. Distilling the knowledge in a neural network. arXiv , 2015. -
  9. [18][18] Minyoung Huh, Andrew Liu, Andrew Owens, and Alexei A Efros. Fighting fake news: Image splice detection via learned self-consistency. In Proceedings of the European Conference on Computer Vision , pages 101-117, 2018. -
  10. [19][19] Yonghyun Jeong, Jongwon Choi, Doyeon Kim, Sehyeon Park, Minki Hong, Changhyun Park, Seungjai Min, and Youngjune Gwon. Dofnet: Depth of field difference learning for detecting image forgery. In Proceedings of the Asian Conference on Computer Vision , 2020. -
  11. [20][20] Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. Progressive growing of GANs for improved quality, stability, and variation. In ICLR , 2018. -
  12. [21][21] Tero Karras, Samuli Laine, and Timo Aila. A stylebased generator architecture for generative adversarial networks. In CVPR , 2019. -
  13. [22][22] Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Analyzing and improving the image quality of StyleGAN. CVPR , 2020. -
  14. [25][25] Patrick Kwon, Jaeseong You, Gyuhyeon Nam, Sungwoo Park, and Gyeongsu Chae. Kodf: A large-scale korean deepfake detection dataset. arXiv preprint arXiv:2103.10094 , 2021. - · arXiv
  15. [27][27] Lingzhi Li, Jianmin Bao, Ting Zhang, Hao Yang, Dong Chen, Fang Wen, and Baining Guo. Face x-ray - for more general face forgery detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 5001-5010, 2020. -
  16. [33][33] Thanh Thi Nguyen, Cuong M Nguyen, Dung Tien Nguyen, Duc Thanh Nguyen, and Saeid Nahavandi. Deep learning for deepfakes creation and detection. arXiv , 2019. -
  17. [35][35] Bo Peng, Wei Wang, Jing Dong, and Tieniu Tan. Optimized 3d lighting environment estimation for image forgery detection. IEEE Trans. Inf. Forensics Secur , 2016. -
  18. [36][36] Andreas Rossler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, and Matthias Nießner. Faceforensics++: Learning to detect manipulated facial images. In ICCV , 2019. -
  19. [37][37] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei. ImageNet Large Scale Visual Recognition Challenge. IJCV , 2015. -
  20. [40][40] Sheng-Yu Wang, Oliver Wang, Richard Zhang, Andrew Owens, and Alexei A Efros. Cnn-generated images are surprisingly easy to spot...for now. In CVPR , 2020. -
  21. [41][41] X. Yang, Y. Li, and S. Lyu. Exposing deep fakes using inconsistent head poses. In ICASSP , 2019. -
  22. [43][43] Fisher Yu, Yinda Zhang, Shuran Song, Ari Seff, and Jianxiong Xiao. Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop. arXiv , 2015. -
  23. [45][45] Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros. Unpaired image-to-image translation using cycle-consistent adversarial networks. In ICCV , 2017.

Cited by

Sorted by citations

Authors
Jeong, Y., Kim, D., Min, S., Joe, S., Gwon, Y., & Choi, J.
Affiliations
  • Samsung SDS (South Korea) (KR)
  • Samsung SDS,Seoul,Korea
  • Chung-Ang University (KR)
ORCID
Venue
2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
Publisher
IEEE
Published
2022-01
Pages
2878-2887
ISSN
2472-6737 (ISSN-L)
DOI
10.1109/wacv51458.2022.00293
OpenAlex
W3196551054
Cited by
106
References
9
Citations by year
2026: 30 · 2025: 39 · 2024: 21 · 2023: 11 · 2022: 5
FWCI
6.72
Type
conference paper
Language
en
Open access
Licence
Locations
  • IEEE Winter Conference on Applications of Computer Vision (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University) (cc-by)
Funders
  • Samsung
  • Korea Institute for Advancement of Technology
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
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
Keywords
deepfake detection, generative adversarial networks, high-pass filtering, unseen domain generalization

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 ›

Are you an author, or do you see a problem with this page? Request a review