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Full 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 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.
Contents
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.
