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 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 commandattacks 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.

Autorentext

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.