Dr. Kun Sun is a professor in the Department of Information Sciences and Technology at George Mason University. He is also the director of Sun Security Laboratory and the associate director of the Center for Secure Information Systems. He received his Ph.D. in Computer Science from North Carolina State University. Before joining GMU, he was an assistant professor in College of William and Mary. He has more than 15 years working experience in both academia and industry; his research work has been funded by government agencies including the NSF, DOD, NSA, DHS, and NIST. His research focuses on systems and network security. He has publishing over 130 conference and journal papers, and two papers won the Best Paper Award. His current research focuses on trustworthy computing environment, software security, moving target defense, network security, smart phone security, cloud security, and AI/ML security.Shu Wang is a Ph.D. Candidate in the Department of Information Sciences and Technology at George Mason University. His research interests lie primarily in the fields of artificial intelligence (AI) and computer security. In particular, his research focuses on the mitigation of attack surfaces in voice processing systems (biometrics security) and open-source software (software security). His past research projects involve computer vision, natural language processing, and digital signal processing. His research papers appear in IEEE S&P, ACM CCS, RAID, IEEE DSN, IEEE INFOCOM, IEEE ICSME, Computers & Security, etc. Previously, He obtained my bachelor’s degree in Communication Engineering and master’s degree in Signal and Information Processing from Nanjing University of Posts and Telecommunications.

• 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.

Dr. Kun Sun is a professor in the Department of Information Sciences and Technology at George Mason University. He is also the director of Sun Security Laboratory and the associate director of the Center for Secure Information Systems. He received his Ph.D. in Computer Science from North Carolina State University. Before joining GMU, he was an assistant professor in College of William and Mary. He has more than 15 years working experience in both academia and industry; his research work has been funded by government agencies including the NSF, DOD, NSA, DHS, and NIST. His research focuses on systems and network security. He has publishing over 130 conference and journal papers, and two papers won the Best Paper Award. His current research focuses on trustworthy computing environment, software security, moving target defense, network security, smart phone security, cloud security, and AI/ML security.Shu Wang is a Ph.D. Candidate in the Department of Information Sciences and Technology at George Mason University. His research interests lie primarily in the fields of artificial intelligence (AI) and computer security. In particular, his research focuses on the mitigation of attack surfaces in voice processing systems (biometrics security) and open-source software (software security). His past research projects involve computer vision, natural language processing, and digital signal processing. His research papers appear in IEEE S&P, ACM CCS, RAID, IEEE DSN, IEEE INFOCOM, IEEE ICSME, Computers & Security, etc. Previously, He obtained my bachelor’s degree in Communication Engineering and master’s degree in Signal and Information Processing from Nanjing University of Posts and Telecommunications.

• 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.

Dr. Kun Sun is a professor in the Department of Information Sciences and Technology at George Mason University. He is also the director of Sun Security Laboratory and the associate director of the Center for Secure Information Systems. He received his Ph.D. in Computer Science from North Carolina State University. Before joining GMU, he was an assistant professor in College of William and Mary. He has more than 15 years working experience in both academia and industry; his research work has been funded by government agencies including the NSF, DOD, NSA, DHS, and NIST. His research focuses on systems and network security. He has publishing over 130 conference and journal papers, and two papers won the Best Paper Award. His current research focuses on trustworthy computing environment, software security, moving target defense, network security, smart phone security, cloud security, and AI/ML security.Shu Wang is a Ph.D. Candidate in the Department of Information Sciences and Technology at George Mason University. His research interests lie primarily in the fields of artificial intelligence (AI) and computer security. In particular, his research focuses on the mitigation of attack surfaces in voice processing systems (biometrics security) and open-source software (software security). His past research projects involve computer vision, natural language processing, and digital signal processing. His research papers appear in IEEE S&P, ACM CCS, RAID, IEEE DSN, IEEE INFOCOM, IEEE ICSME, Computers & Security, etc. Previously, He obtained my bachelor’s degree in Communication Engineering and master’s degree in Signal and Information Processing from Nanjing University of Posts and Telecommunications.

• 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.