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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]).”
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UCF: Uncovering Common Features for Generalizable Deepfake Detection
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“…image forgery have developed novel solutions from different directions: data augmentation [4, 28, 29, 42, 60], frequency clues [17, 33, 34, 37, 52], ID information [13, 23], disentanglement learning [32, 54, 56], designed networks [7, 59], reconstruction learning [3, 50], and 3D decomposition [64].”
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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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