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"Hello, It's Me": Deep Learning-based Speech Synthesis Attacks in the Real World

Authors:

Christian Cianfarani,

Ben Y. ZhaoAuthors Info & Claims

CCS '21: Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security

Pages 235 - 251

https://doi.org/10.1145/3460120.3484742

Published: 13 November 2021 Publication History

Abstract

Advances in deep learning have introduced a new wave of voice synthesis tools, capable of producing audio that sounds as if spoken by a target speaker. If successful, such tools in the wrong hands will enable a range of powerful attacks against both humans and software systems (aka machines). This paper documents efforts and findings from a comprehensive experimental study on the impact of deep-learning based speech synthesis attacks on both human listeners and machines such as speaker recognition and voice-signin systems. We find that both humans and machines can be reliably fooled by synthetic speech, and that existing defenses against synthesized speech fall short. These findings highlight the need to raise awareness and develop new protections against synthetic speech for both humans and machines.

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Cited By

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Index Terms

"Hello, It's Me": Deep Learning-based Speech Synthesis Attacks in the Real World
1. Computing methodologies
  1. Machine learning
2. Security and privacy
  1. Security services
    1. Authentication
      1. Biometrics

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cover image ACM Conferences

CCS '21: Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security

November 2021

3558 pages

ISBN:9781450384544

DOI:10.1145/3460120

General Chairs:
Yongdae Kim
KAIST, Republic of Korea
,
Jong Kim
POSTECH, Republic of Korea
,
Program Chairs:
Giovanni Vigna
University of California, Santa Barbara / VMware, USA
,
Elaine Shi
Carnegie Mellon University, USA

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https://doi.org/10.1109/TQCEBT59414.2024.10545048
Tran HHu JHu W(2024)Biometrics-Based Authenticated Key Exchange With Multi-Factor Fuzzy ExtractorIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.346862419(9344-9358)Online publication date: 2024
https://doi.org/10.1109/TIFS.2024.3468624
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Chen GZhao ZSong FChen SFan LWang FWang J(2023)Towards Understanding and Mitigating Audio Adversarial Examples for Speaker RecognitionIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2022.322067320:5(3970-3987)Online publication date: 1-Sep-2023
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