Concentric Ring Loss for Face Forgery Detection
Abstract
The issue of detecting face forgeries has garnered significant interest in the field of computer vision, primarily driven by the growing social concerns of indistinguishable deepfake images. One of the primary obstacles encountered in the field of deepfake detection is enhancing the discriminative power of learned features. In this paper, we propose a Concentric Ring Loss (CRL) that aims to promote the learning of compressed intra-class features and separated inter-class features inside a model. Specifically, we apply margin penalties in both Euclidean and angular space separately, which serve to increase the separation between real and fake images. Moreover, we introduce a frequency-aware triplet network with a self-developed sample generation strategy, which provides efficient hard triplets for model training. Extensive experiments demonstrate the superiority of our methods over multiple datasets. We show that CRL consistently outperforms the state-of-the-art by a large margin.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Softmax loss has limitations in effectively distinguishing subtle forgery artifacts.
“yet recent studies highlight its limitations in effectively distinguishing subtle forgery artifacts.”
From Evolving from Single-modal to Multi-modal Facial Deepfake Detection: Progress and Challenges (2024) · cites this paper as [151]
Traditional deepfake detection approaches predominantly rely on Softmax loss.
“Traditional deepfake detection approaches predominantly rely on Softmax loss”
From Evolving from Single-modal to Multi-modal Facial Deepfake Detection: Progress and Challenges (2024) · cites this paper as [151]
Cites
Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain
Liu, H., Li, X., Zhou, W., Chen, Y., He, Y., Xue, H., Zhang, W., & Yu, N. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00083 · Cited by 600 · see citing works
Detecting Deepfakes with Self-Blended Images
Shiohara, K., & Yamasaki, T. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01816 · Cited by 503 · see citing works
The Deepfake Detection Challenge (DFDC) Preview Dataset
Dolhansky, B., Howes, R., Pflaum, B., Baram, N., & Ferrer, C. C. (2019) · arXiv · doi:10.48550/arxiv.1910.08854 · Cited by 413 · see citing works
Learning Self-Consistency for Deepfake Detection
Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., & Xia, W. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01475 · Cited by 383 · see citing works
Self-supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detection
Chen, L., Zhang, Y., Song, Y., Liu, L., & Wang, J. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01815 · Cited by 307 · see citing works
Face Forgery Detection by 3D Decomposition
Zhu, X., Wang, H., Fei, H., Lei, Z., & Li, S. Z. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00295 · Cited by 106 · see citing works
TI <sup>2</sup> Net: Temporal Identity Inconsistency Network for Deepfake Detection
Liu, B., Liu, B., Ding, M., Zhu, T., & Yu, X. (2023) · 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv56688.2023.00467 · Cited by 50 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Evolving from Single-modal to Multi-modal Facial Deepfake Detection: Progress and Challenges
Liu, P., Tao, Q., & Zhou, J. T. (2024) · arXiv · doi:10.48550/arxiv.2406.06965 · Cited by 6 · see citing works
Liu, P., Tao, Q., & Zhou, J. (2024) · Proceedings of the 1st ACM Multimedia Workshop on Multi-modal Misinformation Governance in the Era of Foundation Models · doi:10.1145/3689090.3689386 · Cited by 3 · see citing works
- Authors
- Yin, Y., Bai, Y., Wang, Y., & Fu, Y.
- Affiliations
- Case Western Reserve University (US)
- Northeastern University (US)
- Venue
- 2023 IEEE International Conference on Data Mining (ICDM)
- Publisher
- IEEE
- Published
- 2023-12-01
- Pages
- 1505-1510
- ISSN
- 1550-4786 (ISSN-L)
- OpenAlex
- W4391549715
- Cited by
- 2
- References
- 7
- Citations by year
- 2025: 1 · 2024: 1
- FWCI
- 0.21
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
- Keywords
- face forgery detection, deepfake detection, margin-based loss, angular margin
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
