Papers › Leveraging Real Talking Faces via Self-Supervision for Robust Forgery Detection
Leveraging Real Talking Faces via Self-Supervision for Robust Forgery Detection
Alexandros Haliassos, Rodrigo Mira, Stavros Petridis, Maja Pantic
One of the most pressing challenges for the detection of face-manipulated videos is generalising to forgery methods not seen during training while remaining effective under common corruptions such as compression. In this paper, we examine whether we can tackle this issue by harnessing videos of real talking faces, which contain rich information on natural facial appearance and behaviour and are readily available in large quantities online. Our method, termed RealForensics, consists of two stages. First, we exploit the natural correspondence between the visual and auditory modalities in real videos to learn, in a self-supervised cross-modal manner, temporally dense video representations that capture factors such as facial movements, expression, and identity. Second, we use these learned representations as targets to be predicted by our forgery detector along with the usual binary forgery classification task; this encourages it to base its real/fake decision on said factors. We show that our method achieves state-of-the-art performance on cross-manipulation generalisation and robustness experiments, and examine the factors that contribute to its performance. Our results suggest that leveraging natural and unlabelled videos is a promising direction for the development of more robust face forgery detectors.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| DeepFake Detection | FakeAVCeleb | RealForensics | AP | 95.3 | #2 of 13 | Archive leaderboard | report |
| DeepFake Detection | FakeAVCeleb | RealForensics | ROC AUC | 97.1 | #2 of 13 | Archive leaderboard | report |
| DeepFake Detection | FakeAVCeleb | AVBYOL | AP | 73.9 | #8 of 13 | Archive leaderboard | report |
| DeepFake Detection | FakeAVCeleb | AVBYOL | ROC AUC | 59.2 | #8 of 13 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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