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October 16, 2025Open Access

Post-training for Deepfake Speech Detection

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Post-training improves deepfake speech detection accuracy in multilingual models, indicating strong robustness.

Post-training enhances deepfake speech detection, showing significant robustness against unseen deepfake samples.
Models fine-tuned on the Deepfake-Eval-2024 dataset consistently outperform state-of-the-art detectors, showcasing efficacy.
AntiDeepfake models leverage a vast multilingual speech dataset of over 74,000 hours, enhancing generalization.
Adaptation through self-supervised learning and post-training may redefine deepfake detection methodologies.

Ge et al. (2025) studied this question.

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