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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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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
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