Enregistré dans:
| Auteurs principaux: | , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2509.06361 |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866912577674543104 |
|---|---|
| author | Chen, Liping Lee, Kong Aik Ling, Zhen-Hua Wang, Xin Das, Rohan Kumar Toda, Tomoki Li, Haizhou |
| author_facet | Chen, Liping Lee, Kong Aik Ling, Zhen-Hua Wang, Xin Das, Rohan Kumar Toda, Tomoki Li, Haizhou |
| contents | In the era of big data, remarkable advancements have been achieved in personalized speech generation techniques that utilize speaker attributes, including voice and speaking style, to generate deepfake speech. This has also amplified global security risks from deepfake speech misuse, resulting in considerable societal costs worldwide. To address the security threats posed by deepfake speech, techniques have been developed focusing on both the protection of voice attributes and the defense against deepfake speech. Among them, the voice anonymization technique has been developed to protect voice attributes from extraction for deepfake generation, while deepfake detection and watermarking have been utilized to defend against the misuse of deepfake speech. This paper provides a short and concise overview of the three techniques, describing the methodologies, advancements, and challenges. A comprehensive version, offering additional discussions, will be published in the near future. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_06361 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Speaker Privacy and Security in the Big Data Era: Protection and Defense against Deepfake Chen, Liping Lee, Kong Aik Ling, Zhen-Hua Wang, Xin Das, Rohan Kumar Toda, Tomoki Li, Haizhou Audio and Speech Processing In the era of big data, remarkable advancements have been achieved in personalized speech generation techniques that utilize speaker attributes, including voice and speaking style, to generate deepfake speech. This has also amplified global security risks from deepfake speech misuse, resulting in considerable societal costs worldwide. To address the security threats posed by deepfake speech, techniques have been developed focusing on both the protection of voice attributes and the defense against deepfake speech. Among them, the voice anonymization technique has been developed to protect voice attributes from extraction for deepfake generation, while deepfake detection and watermarking have been utilized to defend against the misuse of deepfake speech. This paper provides a short and concise overview of the three techniques, describing the methodologies, advancements, and challenges. A comprehensive version, offering additional discussions, will be published in the near future. |
| title | Speaker Privacy and Security in the Big Data Era: Protection and Defense against Deepfake |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2509.06361 |