Audio Spoof Detection from Theory to Practical Application
Published on 21. May 2026
256 pages
978-1-040-58986-1 (ISBN)
Description
Audio Spoof Detection (ASD) systems play a pivotal role in evaluating whether the input speech signal has been manipulated by an imposter attempting unauthorized access to an authentic user's account or if it genuinely originates from the declared user. Primarily used for person authentication, these systems strive to verify the speaker's claimed identity. Despite substantial technological advancements, recent testing has revealed persistent vulnerabilities to spoofing, commonly referred to as a spoof attack. Various techniques such as mimicry, replay, text to speech (TTS), and voice conversion (VC) are frequently used in ASV systems to execute logical access (LA) or physical access (PA) spoofing attacks. To protect an ASD system from these attacks, many researchers have proposed effective security models as countermeasures. In addition, numerous review papers by different researchers have discussed various countermeasures developed to secure ASD systems. However, there is a notable absence of an authored book that comprehensively addresses this critical research topic, encompassing frontend, backend, dataset, and types of attacks considerations. Therefore, there is an urgent need for a book that can serve as a valuable resource for upcoming researchers, offering insights into securing ASD systems and bridging the existing gap in the literature. Hence, this book represents an effort by the authors in that direction.
More details
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
Illustrations
20 Tables, black and white; 67 Line drawings, black and white; 56 Halftones, black and white; 123 Illustrations, black and white
File size
17,39 MB
ISBN-13
978-1-040-58986-1 (9781040589861)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Mohit Dua | Nidhi Chakravarty | Shelza Dua
Audio Spoof Detection from Theory to Practical Application
Book
05/2026
1st Edition
CRC Press
€136.50
Shipment within 10-20 days
Mohit Dua | Nidhi Chakravarty | Shelza Dua
Audio Spoof Detection from Theory to Practical Application
Book
05/2026
1st Edition
CRC Press
€56.00
Shipment within 10-20 days
Persons
Mohit Dua earned his Ph.D. in Automatic Speech Recognition from the National Institute of Technology, Kurukshetra, India, in 2018. He is presently working as an assistant professor in the Department of Computer Engineering at NIT Kurukshetra, India. He has more than 17 years of teaching and research experience. He is a member of Institute of Electrical and Electronics Engineers (IEEE), and life member of the Computer Society of India (CSI) and Indian Society for Technical Education (ISTE). His research interests include speech processing, chaos-based cryptography, information security, theory of formal languages, statistical modelling and natural language processing. He has published approximately 100+ research papers including abroad paper presentations in the USA, Canada, Australia, Singapore, Mauritius, and Dubai.
Nidhi Chakravarty earned her Ph.D. in Audio Spoof Detection from the National Institute of Technology, Kurukshetra, India, in 2024. She is presently working as an assistant professor in the Department of Computer Science and Engineering at Thapar Institute of Engineering and Technology, Patiala, India. She has published approximately 20+ research papers in various reputed journals and international conferences.
Shelza Dua earned her Ph.D. in Image Encryption from Banasthali Vidyapith, Banasthali Rajasthan, India, in 2019. She is presently working as a research associate in the Department of Electrical Engineering at NIT Kurukshetra, India. She has more than 20+ years of teaching and research experience. She is a life member of IETE. Her research interests include chaos-based cryptography and image encryption. She has published approximately 30+ research papers in various reputed journals and international conferences.
Nidhi Chakravarty earned her Ph.D. in Audio Spoof Detection from the National Institute of Technology, Kurukshetra, India, in 2024. She is presently working as an assistant professor in the Department of Computer Science and Engineering at Thapar Institute of Engineering and Technology, Patiala, India. She has published approximately 20+ research papers in various reputed journals and international conferences.
Shelza Dua earned her Ph.D. in Image Encryption from Banasthali Vidyapith, Banasthali Rajasthan, India, in 2019. She is presently working as a research associate in the Department of Electrical Engineering at NIT Kurukshetra, India. She has more than 20+ years of teaching and research experience. She is a life member of IETE. Her research interests include chaos-based cryptography and image encryption. She has published approximately 30+ research papers in various reputed journals and international conferences.
Content
Author Biographies. Foreword. Preface. Chapter 1: Introduction. 1.1 Background. 1.2 Definition. 1.3 History. 1.4 Real and Fake Audio. 1.5 Emerging Threats in Voice-Based Fraud. 1.6 How AI Voice Scams are Taking Place. 1.7 Book Organization. Chapter 2: Audio Signal Processing. 2.1 Human Hearing. 2.2 Anatomy of the Auditory System. 2.3 How We Hear. 2.4 Psychoacoustics: The Science of Sound Perception. 2.5 What Are Filters?. 2.6 Hearing and Sound Waves. 2.7 Basic Qualities of Sound. 2.8 Digital Audios. 2.9 Audio Preprocessing Techniques. 2.10 Application of Audio Processing. 2.11 Attacks on ASV. 2.12 Conclusion. Chapter 3: Feature Extraction. 3.1 Introduction. 3.2 Fundamentals Used in Audio Signal Processing. 3.3 Taxonomy of Audio Features. 3.4 Perceptual Features. 3.5 Statistical and Temporal Features. 3.6 Challenges in Audio Feature Extraction. 3.7 Future Trends. 3.8 Conclusion. Chapter 4: Backend Classification. 4.1 Introduction. 4.2 Backend Classification Strategies for ASD. 4.3 Conclusion. Chapter 5: Attacks on ASV System. 5.1 Introduction. 5.2 History of Spoof Attack. 5.3 Fake Audio Generation. 5.4 Attacks on ASV. 5.5 Conclusion. Chapter 6: Data Augmentation. 6.1 Introduction. 6.2 Data Augmentation Techniques. 6.3 Applications of Data Augmentation in Speech Processing. 6.4 Conclusion. Chapter 7: Evaluation Metrics. 7.1 Introduction. 7.2 Overview of Evaluation Metrics. 7.3 Conclusion. Chapter 8: Datasets in Audio Spoof Detection. 8.1 Introduction. 8.2 Dataset Characteristics. 8.3 Datasets. 8.4 Dataset Generation Techniques. 8.5 Challenges in Audio Spoof Detection Dataset Design. 8.6 Future Directions for Dataset Development. 8.7 Conclusion. Chapter 9: Recent Trends and Open Issues. 9.1 Generalization and Application of the Proposed Work. 9.2 Suggestions for Future Work. Chapter 10: Implementation of the ASD System using Python. 10.1 Introduction. 10.2 System Requirements. 10.3 Dataset Handling. 10.4 Feature Extraction. 10.5 Machine Learning and Deep Learning Models for Audio Classification. Index.
