ID-Reveal: Identity-aware DeepFake Video Detection
Open accessAbstract
A major challenge in DeepFake forgery detection is that state-of-the-art algorithms are mostly trained to detect a specific fake method. As a result, these approaches show poor generalization across different types of facial manipulations, e.g., from face swapping to facial reenactment. To this end, we introduce ID-Reveal, a new approach that learns temporal facial features, specific of how a person moves while talking, by means of metric learning coupled with an adversarial training strategy. The advantage is that we do not need any training data of fakes, but only train on real videos. Moreover, we utilize high-level semantic features, which enables robustness to widespread and disruptive forms of post-processing. We perform a thorough experimental analysis on several publicly available benchmarks. Compared to state of the art, our method improves generalization and is more robust to low-quality videos, that are usually spread over social networks. In particular, we obtain an average improvement of more than 15% in terms of accuracy for facial reenactment on high compressed videos.
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Prior POI methods rely on video-only features, sometimes complemented by categorical information, neglecting audio information.
“These methods present some limitations: in particular, they rely on video-only features, sometimes complemented by categorical information, neglecting precious audio information.”
From Audio-Visual Person-of-Interest DeepFake Detection (2023) · cites this paper as [12]
The Identity-based features section (page not available) describes several earlier approaches based on facial appearance and movement, supporting the visual-feature part of the claim. It also describes an approach that compares mouth movements with spoken phonemes, so the paper does not establish that prior methods uniformly neglect audio. The cited passages do not substantiate the added point about categorical information. (LLM, conf=0.82)
Experimental — accuracy not yet measuredID-specific methods that use biometric signals to identify unique facial motion patterns have limited ability to generalize to new identities.
“Another area of research makes use of biometric signals to identify unique facial motion patterns in a particular person, however, the ability of such ID-specific methods to generalize to new identities is limited [46].”
From Attention-based Multimodal learning framework for Generalized Audio-Visual Deepfake Detection (2023) · cites this paper as [46]
The “Identity-based features” section describes methods that use individual biometric traits, including distinctive facial and head movements. It does not say that these methods have limited ability to generalize to new identities; the “Generalization and robustness analysis” section instead discusses generalization across manipulation methods. Page numbers are unavailable in our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredCharacterizing pristine faces based on semantic features such as soft biometrics is particularly effective for POI-based detection.
“This approach turns out to be particularly effective if the characterization of pristine faces is based on semantic features, such as soft biometrics, leading to Person-of-Interest (POI) based detection [3,5-7,12,20].”
From Audio-Visual Person-of-Interest DeepFake Detection (2023) · cites this paper as [12]
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- Authors
- Cozzolino, D., Rossler, A., Thies, J., Niesner, M., & Verdoliva, L.
- Affiliations
- University of Naples Federico II (IT)
- Technical University of Munich
- Technical University of Munich (DE)
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 15088-15097
- OpenAlex
- W3110828385
- Semantic Scholar
- 5b422386d8944f7ab10ec5e2067d596b8cc8a3bb
- Cited by
- 221
- References
- 52
- Citations by year
- 2026: 33 · 2025: 49 · 2024: 53 · 2023: 52 · 2022: 27 · 2021: 7
- FWCI
- 11.30
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Advanced Research Projects Agency
- 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
- deepfake detection, face manipulation, metric learning, adversarial training, face swapping, semantic features
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