000 05949nam a22003975i 4500
001 397712
003 ES-MaUEC
005 20240104162608.0
006 a||||fo|||| 00| 0
007 cr nn 008mamaa
008 231030s2024 sz | s |||| 0|eng d
020 _a9783031447488
024 7 _a10.1007/978-3-031-44748-8
_2doi
040 _aES-MaUEC
_bspa
_cES-MaUEC
050 0 4 _aQA76.9.A25
_b2024
050 0 4 _aJC596-596.2
_b2024
100 1 _aSun, Kun.
_eautor
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 0 0 _aSecure Voice Processing Systems against Malicious Voice Attacks
_cby Kun Sun, Shu Wang
250 _afirst edition 2024
264 1 _aCham
_c2024
_bSpringer International Publishing
300 _a1 recurso en línea
336 _atexto
_btxt
_2rdacontent
337 _aelectrónico
_bc
_2rdamedia
338 _arecurso electrónico
_bcr
_2rdacarrier
347 _atext file
_bPDF
490 0 _aSpringerBriefs in Computer Science
_x2191-5776
505 0 _a1 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.
520 _aThis 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 against malicious voice attacks under different scenarios, by utilizing time-domain signal waves, frequency-domain spectrum features and acoustic physical attributes. First, the authors uncover the modulated replay attack, which uses an inverse filter to compensate for the spectrum distortion caused by the replay attacks to bypass the existing spectrum-based defenses. The authors also provide an effective defense method that utilizes both the time-domain artifacts and frequency-domain distortion to detect the modulated replay attacks. Second, the book introduces a secure automatic speech recognition system for driverless car to defeat adversarial voice command attacks launched from car loudspeakers, smartphones and passengers. Third, it provides an acoustic compensation system design to reduce the effects from the spectrum reduction attacks, by the audio spectrum compensation and acoustic propagation principle. Finally, the authors conclude with their research effort on defeating the malicious voice attacks and provide insights into more secure voice processing systems. This book is intended for security researchers, computer scientists and electrical engineers who are interested in the research areas of biometrics, speech signal processing, IoT security and audio security. Advanced-level students who are studying these topics will benefit from this book as well.
856 4 0 _uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-44748-8
_zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _2lcc
_cLE
988 _aSpringer_Computer_2024
999 _c397712
_d397712