We introduce a post-training approach that adapts self-supervised learning (SSL) models for deepfake speech detection by bridging the gap between general pre-training and domain-specific fine-tuning. We present AntiDeepfake models, a series of post-trained models developed using a large-scale multilingual speech dataset containing over 56,000 hours of genuine speech and 18,000 hours of speech with various artifacts in over one hundred languages. Experimental results show that the post-trained models already exhibit strong robustness and generalization to unseen deepfake speech. When they are further fine-tuned on the Deepfake-Eval-2024 dataset, these models consistently surpass existing state-of-the-art detectors that do not leverage post-training. Model checkpoints and source code are available online.
Post-training for Deepfake Speech Detection
AntiDeepfake models, developed through post-training and fine-tuning on a large multilingual speech dataset, exhibit strong robustness and outperform existing state-of-the-art deepfake speech detectors.
- Year
- 2025
- Venue
- arXiv 2025
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- 4
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- Abstract onlyARXIV-DEFAULT
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- arxiv.org/abs/2506.21090ARXIV-DEFAULT
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