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; (ii) video modality: temporal dropout, temporal repeat, clip-level blending; (iii) audio modality: band-pass filters, timeand pitch-shifting, RawBoost.
“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]
Section 4.2 (Implementation Details; page unavailable) lists Gauss blur and Random Resized Crop as image augmentations. It also lists “quality compression,” but does not specify JPEG compression. The video and audio examples have separate citations and are not attributed to this source. (LLM, conf=0.84)
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Leveraging High-Frequency Diversified Augmentation for general deepfake detection
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An attention-erasing stripe pyramid network for face forgery detection
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Centroid-based Contrastive Consistency Learning for transferable deepfake detection
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Penny-Wise and Pound-Foolish in AI-Generated Image Detection
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Revisiting Face Forgery Detection: From Facial Representation to Forgery Detection
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Learn from Noise: Detecting Deepfakes via Regional Noise Consistency
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Consistent Learning of Multiple Paths Using Frequency-Aware Clues for Face Forgery Detection
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Multi-level Distributional Discrepancy Enhancement for Cross Domain Face Forgery Detection
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TMFD: Two-Stage Meta-learning Feature Disentanglement Framework for DeepFake Detection
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MVFNet: Multipurpose Video Forensics Network using Multiple Forms of Forensic Evidence
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Context-Aware Deepfake Detection for Securing AI-Driven Financial Transactions
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Self-citationMulti-Cue Fusion for Forgery Detection: a Spatial-Frequency and Chromatic Approach
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Rethinking Individual Fairness in Deepfake Detection
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RoGA: Towards Generalizable Deepfake Detection through Robust Gradient Alignment
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Seeing Through the Blur: Unlocking Defocus Maps for Deepfake Detection
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READFake: Reflection and Environment-Aware DeepFake Detection
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Revealing the compactness of real samples via image reconstruction for deepfake detection
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Where to Focus: Central Attention-Based Face Forgery Detection
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Deepfake Video Traceability and Authentication via Source Attribution
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Quality-Agnostic Deepfake Detection With Saliency-Guided Restoration and Adaptive Fusion
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- 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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