Audio Spoof Detection from Theory to Practical Application
-
GET 10% OFF
- Publisher's listprice GBP 44.99
-
20 312 Ft (19 345 Ft + 5% VAT)
The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.
- Discount 10% (cc. 2 031 Ft off)
- Discounted price 18 281 Ft (17 411 Ft + 5% VAT)
18 281 Ft
Availability
Not yet published.
Why don't you give exact delivery time?
Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.
Product details:
- Edition number 1
- Publisher CRC Press
- Date of Publication 21 May 2026
- ISBN 9781032912646
- Binding Paperback
- No. of pages256 pages
- Size 234x156 mm
- Language English
- Illustrations 123 Illustrations, black & white; 56 Halftones, black & white; 67 Line drawings, black & white; 20 Tables, black & white 700
Categories
Short 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.
MoreLong 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.
MoreTable of Contents:
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.
More
