Audio Spoof Detection from Theory to Practical Application

1. Auflage 2026
ISBN: 978-1-032-91053-6
Verlag: Taylor & Francis Ltd

Buch, Englisch, 288 Seiten, Format (B × H): 156 mm x 234 mm

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 employed in ASV systems to execute logical access (LA) or physical access (PA) spoofing attacks. To secure an ASD system from these attacks many research have given many good security models as countermeasures. Also, 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 serves as a valuable resource for upcoming researchers, offering insights into securing ASD systems and bridging the existing gap in the literature. Hence, this book is an effort by the authors in such direction.

Zielgruppe

Professional Practice & Development, Professional Reference, and Professional Training

Autoren/Hrsg.

Dua, Mohit

Chakravarty, Nidhi

Dua, Shelza

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Tonsignalverarbeitung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Spracherkennung, Sprachverarbeitung
• Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung Netzwerksicherheit
• Mathematik | Informatik EDV | Informatik Professionelle Anwendung Digitale Musik
• Mathematik | Informatik EDV | Informatik Technische Informatik Computersicherheit Datensicherheit, Datenschutz
• Mathematik | Informatik EDV | Informatik Technische Informatik Computersicherheit Kryptographie, Datenverschlüsselung

Inhaltsverzeichnis

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. 1.8 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 of 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 Automatic Spoofing Detection. 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. 10.6 Conclusion.

Über Autor(innen)

Mohit Dua: He received Ph.D. in the area of Automatic Speech Recognition from National Institute of Technology, Kurukshetra, India in 2018. He is presently working as Assistant Professor in 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 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 including USA, Canada, Australia, Singapore, Mauritius and Dubai.

Nidhi Chakravarty: She received Ph.D. in the area of Audio Spoof Detection from National Institute of Technology, Kurukshetra, India in 2024. She is presently working as Assistant Professor in 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: She received Ph.D. in the area of Image Encryption from Banasthali Vidyapith, Banasthali Rajasthan, India in 2019. She is presently working as Research Associate in 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.