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Implicit Identity Leakage: The Stumbling Block to Improving Deepfake Detection Generalization
Open accessAbstract
In this paper, we analyse the generalization ability of binary classifiers for the task of deepfake detection. We find that the stumbling block to their generalization is caused by the unexpected learned identity representation on images. Termed as the Implicit Identity Leakage, this phenomenon has been qualitatively and quantitatively verified among various DNNs. Furthermore, based on such understanding, we propose a simple yet effective method named the ID-unaware Deepfake Detection Model to reduce the influence of this phenomenon. Extensive experimental results demonstrate that our method outperforms the state-of-the-art in both in-dataset and cross-dataset evaluation. The code is available at https://github.com/megvii-research/CADDM.
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“Despite these efforts, recent methods still face challenges, often learning dataset-specific cues that fail to detect unseen forgery samples.”
From Towards More General Video-based Deepfake Detection through Facial Component Guided Adaptation for Foundation Model (2024) · cites this paper as [9]
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“Using Adam as the optimizer can be considered a common setting, and a substantial number of deepfake detection methods in existing literature have adopted this configuration (paper [20, 27, 26, 3, 12]).”
From DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection (2023) · cites this paper as [12]
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Liu, C., Wang, L., & Guo, Z. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11463764
Superficial Texture Suppression Network for Generalized Deepfake Detection
Zhang, L., Sun, Z., Guo, J., & Wang, S. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11461834
FC-MoE: Flip Consistent Mixture of Experts are Good Learners for Unified Face Attack Detection
Qin, L., Jiang, N., Qiu, Y., Zhang, Y., Zeng, D., Hu, J., & Deng, W. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11462110
Deepfake in Image and Video Processing
Gao, B., Wang, S., Li, X., Zhang, W., Cui, Y., & Qi, C. (2025) · Studies in computational intelligence · doi:10.1007/978-3-032-12891-1_2 · Cited by 1
Serackis, A., Jankauskas, M., Grubinskienė, A., & Abromavičius, V. (2026) · Applied Sciences · doi:10.3390/app16115364
Improving Generalization in Deepfake Detection With Feature-Space Adversarial Consistency
Singh, P., & Pankajakshan, V. (2026) · IEEE Signal Processing Letters · doi:10.1109/lsp.2026.3698377
Metadata
- Authors
- Dong, S., Wang, J., Ji, R., Liang, J., Fan, H., & Ge, Z.
- Affiliations
- Megvii (China) (CN)
- Venue
- 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2023-06
- Pages
- 3994-4004
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4386071484
- Cited by
- 220
- References
- 75
- Citations by year
- 2026: 57 · 2025: 95 · 2024: 62 · 2023: 6
- FWCI
- 23.53
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (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 › Signal Processing › Biometric Identification and Security
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- deepfake detection, generalization, identity representation, cross-dataset evaluation, binary classification
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