# Post-training for Deepfake Speech Detection

arXiv: https://arxiv.org/abs/2506.21090 · HTML: https://mlsanity.com/paper/2506.21090/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2025.

## Built on by (most cited first, 11 of 11)

- [WaveSP-Net: Learnable Wavelet-Domain Sparse Prompt Tuning for Speech Deepfake Detection](https://mlsanity.com/paper/2510.05305/) (2025)
- [Attention-based Mixture of Experts for Robust Speech Deepfake Detection](https://mlsanity.com/paper/2509.17585/) (2025)
- [AUDDT: A Unified Benchmark Toolkit for Audio and Speech Deepfake Detectors](https://mlsanity.com/paper/2509.21597/) (2025)
- [Alethia: A Foundational Encoder for Voice Deepfakes](https://mlsanity.com/paper/2605.00251/) (2026)
- [DetectZoo: A Unified Toolkit for AI-Generated Content Detection Across Text, Audio, and Image Modalities](https://mlsanity.com/paper/2606.04205/) (2026)
- [Teffic-Audio: Tell Fact from Fiction](https://mlsanity.com/paper/2607.28351/) (2026)
- [ICLAD: In-Context Learning with Comparison-Guidance for Audio Deepfake Detection](https://mlsanity.com/paper/2604.16749/) (2026)
- [Trajectory Dynamics in Self-Supervised Learning Latent Space for Audio Deepfake Detection](https://mlsanity.com/paper/2608.13817/) (2026)
- [From Scores to Evidence: Auditable Decisions Can Improve Speech Deepfake Detection](https://mlsanity.com/paper/2609.08899/) (2026)
- [VietPrism: A large-scale Vietnamese speech and deepfake corpus with diverse dialects and code-switching](https://mlsanity.com/paper/2609.30005/) (2026)
- [Task-Aware Joint Pruning and Distillation for Efficient Audio Deepfake Detection](https://mlsanity.com/paper/2610.05264/) (2026)

## Abstract (arXiv)

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.