Audio Spoof Detection from Theory to Practical Application

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.

Biography

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.