A Comparative Study on Proactive and Passive Detection of Deepfake Speech
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866915348395065344 |
|---|---|
| author | Wu, Chia-Hua Ge, Wanying Wang, Xin Yamagishi, Junichi Tsao, Yu Wang, Hsin-Min |
| author_facet | Wu, Chia-Hua Ge, Wanying Wang, Xin Yamagishi, Junichi Tsao, Yu Wang, Hsin-Min |
| contents | Solutions for defending against deepfake speech fall into two categories: proactive watermarking models and passive conventional deepfake detectors. While both address common threats, their differences in training, optimization, and evaluation prevent a unified protocol for joint evaluation and selecting the best solutions for different cases. This work proposes a framework to evaluate both model types in deepfake speech detection. To ensure fair comparison and minimize discrepancies, all models were trained and tested on common datasets, with performance evaluated using a shared metric. We also analyze their robustness against various adversarial attacks, showing that different models exhibit distinct vulnerabilities to different speech attribute distortions. Our training and evaluation code is available at Github. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_14398 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Comparative Study on Proactive and Passive Detection of Deepfake Speech Wu, Chia-Hua Ge, Wanying Wang, Xin Yamagishi, Junichi Tsao, Yu Wang, Hsin-Min Sound Solutions for defending against deepfake speech fall into two categories: proactive watermarking models and passive conventional deepfake detectors. While both address common threats, their differences in training, optimization, and evaluation prevent a unified protocol for joint evaluation and selecting the best solutions for different cases. This work proposes a framework to evaluate both model types in deepfake speech detection. To ensure fair comparison and minimize discrepancies, all models were trained and tested on common datasets, with performance evaluated using a shared metric. We also analyze their robustness against various adversarial attacks, showing that different models exhibit distinct vulnerabilities to different speech attribute distortions. Our training and evaluation code is available at Github. |
| title | A Comparative Study on Proactive and Passive Detection of Deepfake Speech |
| topic | Sound |
| url | https://arxiv.org/abs/2506.14398 |