AntiFake: Using Adversarial Audio to Prevent Unauthorized Speech Synthesis-Reference-Cited by-同舟云学术

AntiFake: Using Adversarial Audio to Prevent Unauthorized Speech Synthesis

Published:2023-11-15 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security
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Author:

Yu Zhiyuan¹^ORCID,Zhai Shixuan¹^ORCID,Zhang Ning¹^ORCID

Affiliation:

1. Washington University in St. Louis, St. Louis, MO, USA

Funder

Army Research Office

National Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3576915.3623209

Reference67 articles.

1. Hadi Abdullah et al. 2019. Practical Hidden Voice Attacks against Speech and Speaker Recognition Systems. In 26th Annual Network and Distributed System Security Symposium, NDSS 2019, San Diego, California, USA, February 24-27, 2019.

2. Hadi Abdullah et al. 2021. Hear "No Eil", See "Kenansville"*: Efficient and transferable black-box attacks on speech recognition and voice identification systems. In 2021 IEEE Symposium on Security and Privacy (SP). IEEE, 712--729.

3. Muhammad Ejaz Ahmed et al. 2020. Void: A fast and light voice liveness detection system. In 29th USENIX Security Symposium (USENIX Security 20). 2685--2702.

4. James Betker. 2022. TorToiSe TTS. https://github.com/neonbjb/tortoise-tts.

5. Logan Blue et al. 2022. Who Are You (I Really Wanna Know)? Detecting Audio DeepFakes Through Vocal Tract Reconstruction. In 31st USENIX Security Symposium, USENIX Security 2022. USENIX Association, 2691--2708.

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