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Secure Voice Processing Systems against Malicious Voice Attacks

This book provides readers with the basic understanding regarding the threats to the voice processing systems, the state-of-the-art defense methods as well as the current research results on securing voice processing systems. It also introduces three mechanisms to secure the voice processing systems...

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Hlavní autoři: Sun, Kun (Autor), Wang, Shu (Autor)
Korporativní autor: SpringerLink (Online service)
Médium: Elektronický zdroj E-kniha
Jazyk:angličtina
Vydáno: Cham : Springer Nature Switzerland : Imprint: Springer, 2024.
Vydání:1st ed. 2024.
Edice:SpringerBriefs in Computer Science,
Témata:
Data protection > Law and legislation.
Biometric identification.
Privacy.
Biometrics.
On-line přístup:https://doi.org/10.1007/978-3-031-44748-8
Tagy: Přidat tag
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Obsah:
  • 1 Introduction
  • 1.1 Overview
  • 1.2 Background
  • 1.2.1 Audio Signal Processing
  • 1.2.2 Voice Processing Systems
  • 1.2.3 Attacks on Speaker Verification Systems
  • 1.2.4 Attacks on Speech Recognition Systems
  • 1.3 Book Structure
  • References . .
  • 2 Modulated Audio Replay Attack and Dual-Domain Defense
  • 2.1 Introduction
  • 2.2 Modulated Replay Attacks
  • 2.2.1 Impacts of Replay Components
  • 2.2.2 Attack Overview
  • 2.2.3 Modulation Processor
  • 2.2.4 Inverse Filter Estimation
  • 2.2.5 Spectrum Processing
  • 2.3 Countermeasure: Dual-domain Detection
  • 2.3.1 Defense Overview
  • 2.3.2 Time-domain Defense
  • 2.3.3 Frequency-domain Defense
  • 2.3.4 Security Analysis
  • 2.4 Evaluation
  • 2.4.1 Experiment Setup
  • 2.4.2 Effectiveness of Modulated Replay Attacks
  • 2.4.3 Effectiveness of Dual-Domain Detection
  • 2.4.4 Robustness of Dual-Domain Detection
  • 2.4.5 Overhead of Dual-Domain Detection
  • 2.5 Conclusion
  • Appendix 2.A: Mathematical Proof of Ringing Artifacts in Modulated Replay Audio
  • Appendix 2.B: Parameters in Detection Methods
  • Appendix 2.C: Inverse Filter Implementation
  • Appendix 2.D: Classifiers in Time-Domain Defense
  • References
  • 3 Secure Voice Processing Systems for Driverless Vehicles
  • 3.1 Introduction
  • 3.2 Threat Model and Assumptions
  • 3.3 System Design
  • 3.3.1 System Overview
  • 3.3.2 Detecting Multiple Speakers
  • 3.3.3 Identifying Human Voice
  • 3.3.4 Identifying Driver’s Voice
  • 3.4 Experimental Results
  • 3.4.1 Accuracy on Detecting Multiple Speakers
  • 3.4.2 Accuracy on Detecting Human Voice
  • 3.4.3 Accuracy on Detecting Driver’s Voice
  • 3.4.4 System Robustness
  • 3.4.5 Performance Overhead
  • 3.5 Discussions
  • 3.6 Conclusion
  • References
  • 4 Acoustic Compensation System against Adversarial Voice Recognition
  • 4.1 Introduction
  • 4.2 Threat Model
  • 4.2.1 Spectrum Reduction Attack
  • 4.2.2 Threat Hypothesis
  • 4.3 System Design
  • 4.3.1 Overview
  • 4.3.2 Spectrum Compensation Module
  • 4.3.3 Noise Addition Module
  • 4.3.4 Adaptation Module
  • 4.4 Evaluations
  • 4.4.1 Experiment Setup
  • 4.4.2 ACE Evaluation
  • 4.4.3 Spectrum Compensation Module Evaluation
  • 4.4.4 Noise Addition Module Evaluation
  • 4.4.5 Adaptation Module Evaluation
  • 4.4.6 Overhead
  • 4.5 Residual Error Analysis
  • 4.5.1 Types of ASR Inference Errors
  • 4.5.2 Error Composition Analysis
  • 4.6 Discussions
  • 4.6.1 Multipath Effect and Audio Quality Improvement
  • 4.6.2 Usability
  • 4.6.3 Countering Attack Variants
  • 4.6.4 Limitations
  • 4.7 Conclusion
  • Appendix 4.A: Echo Module
  • Appendix 4.B: ACE Performance tested with CMU Sphinx
  • Appendix 4.C: ACE Performance against Attack Variants
  • References
  • 5 Conclusion and Future Work
  • 5.1 Conclusion
  • 5.2 Future Work
  • References.

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