Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.09869 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909347535126528 |
|---|---|
| author | Oiso, Hideyuki Matsunaga, Yuto Kakizaki, Kazuya Miyagawa, Taiki |
| author_facet | Oiso, Hideyuki Matsunaga, Yuto Kakizaki, Kazuya Miyagawa, Taiki |
| contents | We study test-time domain adaptation for audio deepfake detection (ADD), addressing three challenges: (i) source-target domain gaps, (ii) limited target dataset size, and (iii) high computational costs. We propose an ADD method using prompt tuning in a plug-in style. It bridges domain gaps by integrating it seamlessly with state-of-the-art transformer models and/or with other fine-tuning methods, boosting their performance on target data (challenge (i)). In addition, our method can fit small target datasets because it does not require a large number of extra parameters (challenge (ii)). This feature also contributes to computational efficiency, countering the high computational costs typically associated with large-scale pre-trained models in ADD (challenge (iii)). We conclude that prompt tuning for ADD under domain gaps presents a promising avenue for enhancing accuracy with minimal target data and negligible extra computational burden. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_09869 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Prompt Tuning for Audio Deepfake Detection: Computationally Efficient Test-time Domain Adaptation with Limited Target Dataset Oiso, Hideyuki Matsunaga, Yuto Kakizaki, Kazuya Miyagawa, Taiki Sound Artificial Intelligence Cryptography and Security Machine Learning Audio and Speech Processing We study test-time domain adaptation for audio deepfake detection (ADD), addressing three challenges: (i) source-target domain gaps, (ii) limited target dataset size, and (iii) high computational costs. We propose an ADD method using prompt tuning in a plug-in style. It bridges domain gaps by integrating it seamlessly with state-of-the-art transformer models and/or with other fine-tuning methods, boosting their performance on target data (challenge (i)). In addition, our method can fit small target datasets because it does not require a large number of extra parameters (challenge (ii)). This feature also contributes to computational efficiency, countering the high computational costs typically associated with large-scale pre-trained models in ADD (challenge (iii)). We conclude that prompt tuning for ADD under domain gaps presents a promising avenue for enhancing accuracy with minimal target data and negligible extra computational burden. |
| title | Prompt Tuning for Audio Deepfake Detection: Computationally Efficient Test-time Domain Adaptation with Limited Target Dataset |
| topic | Sound Artificial Intelligence Cryptography and Security Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2410.09869 |