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CORE: Consistent Representation Learning for Face Forgery Detection
Open accessAbstract
Face manipulation techniques develop rapidly and arouse widespread public concerns. Despite that vanilla convolutional neural networks achieve acceptable performance, they suffer from the overfitting issue. To relieve this issue, there is a trend to introduce some erasing-based augmentations. We find that these methods indeed attempt to implicitly induce more consistent representations for different augmentations via assigning the same label for different augmented images. However, due to the lack of explicit regularization, the consistency between different representations is less satisfactory. Therefore, we constrain the consistency of different representations explicitly and propose a simple yet effective framework, COnsistent REpresentation Learning (CORE). Specifically, we first capture the different representations with different augmentations, then regularize the cosine distance of the representations to enhance the consistency. Extensive experiments (in-dataset and cross-dataset) demonstrate that CORE performs favorably against state-of-the-art face forgery detection methods. Our code is available at https://github.com/niyunsheng/CORE.
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“Depending on specific modalities, augmentation operations are various, including (i) image modality: Gaussian blur, JPEG compression, random crop [159, 160]; (ii) video modality: temporal dropout, temporal repeat, clip-level blending [161]; (iii) audio modality: band-pass filters, timeand pitch-shifting, RawBoost [151, 154].”
From Deepfake detection across image, video, and audio: a comprehensive survey with empirical evaluation of generalization and robustness (2026) · cites this paper as [159]
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Guo, H., Wang, L., Li, B., & Guo, Z. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-9958-2_20
Representation Alignment For Deepfake Detection
Li, Z., Tang, W., Gao, S., Wang, Y., & Wang, S. (2025) · International Conference on Computing and Artificial Intelligence (ICCAI) · doi:10.1109/iccai66501.2025.00109
MS-FPR: Optimized Multi-Scale Training Strategy for Reducing False Positives in Deepfake Detection
Xing, L., Zhou, W., Fang, Z., Tang, K., He, Y., Huangfu, M., Zhang, W., & Yu, N. (2025) · IEEE International Conference on Privacy Computing and Data Security (PCDS) · doi:10.1109/pcds65695.2025.00029
Enhancing Face Forgery Detection with Augmented Feature Distillation
He, Y. L., Zhou, W., Zhao, H., Fang, Z., Li, X., Huangfu, M., Zhang, W., & Yu, N. (2025) · IEEE International Conference on Privacy Computing and Data Security (PCDS) · doi:10.1109/pcds65695.2025.00025
Liu, J., Shi, H., Zhang, Y., & Zhang, X. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3754984 · Cited by 1
Cognitive Inspired Generalization Boosting for Face Forgery Detection
Huang, Y., & Yang, H. (2025) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme59968.2025.11209182
Sample Based Contrastive Learning for DeepFake Detection
Shao, C., Zhang, F., Wang, J., & Zhang, B. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-5006-4_86
Aggregated Distinguishable Feature Learning for Generalized Deepfake Detection
Yan, B., Xu, C., & Li, C. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228889
MAP-Mamba: Multi-Artifacts Perception Mamba for Generalizable Face Forgery Detection
Wang, C., He, Z., Hu, X., Guan, W., Wang, W., & Fu, Z. (2026) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2026.3651948
Quality-Agnostic Deepfake Detection With Saliency-Guided Restoration and Adaptive Fusion
Si, X., Li, L., Tang, Z., Li, B., Zhang, L., Yuan, K., Liu, H., & Tian, Q. C. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3653480 · Cited by 1
Imperceptible Face Forgery Attack via Adversarial Semantic Mask
Wang, Y., Su, Q., Liu, D., & Peng, C. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-5676-2_26
Metadata
- Authors
- Ni, Y., Meng, D., Yu, C., Quan, C., Ren, D., & Zhao, Y.
- Affiliations
- Tsinghua University (CN)
- University of Science and Technology of China (CN)
- Meituan (China) (CN)
- Venue
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2022-06
- Pages
- 12-21
- OpenAlex
- W4292793883
- Cited by
- 136
- References
- 52
- Citations by year
- 2026: 46 · 2025: 50 · 2024: 29 · 2023: 10 · 2022: 1
- FWCI
- 8.16
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- 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 › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Keywords
- face forgery detection, face manipulation, representation learning, consistency regularization, overfitting, cross-dataset 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.
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