Adversarial Example Detection and Mitigation Using Machine Learning
Language: English
Published by Springer, 2026
- Hardcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Condition: New
US$ 265.67
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Add to basketItem description from seller
Druck auf Anfrage Neuware - Printed after ordering - This book offers a comprehensive exploration of the emerging threats and defense strategies in adversarial machine learning and AI security. It covers a broad range of topics, from federated learning attacks, adversarial defenses, biometric vulnerabilities,and security weaknesses in generative AI to quantum threats and ethical considerations. It also brings together leading researchers to provide an in-depth and multifaceted perspective.As artificial intelligence systems become increasingly integrated into critical sectors such as healthcare, finance, transportation, and national security, understanding and mitigating adversarial risks has never been more crucial. Each chapter delivers not only a detailed analysis of current challenges, but it also includes insights into practical mitigation techniques, future trends, and real-world applications.This book is intended for researchers and graduate students working in machine learning, cybersecurity, and related disciplines. Security professionals will also find this book to be a valuable reference for understanding the latest advancements, defending against sophisticated adversarial threats, and contributing to the development of more robust, trustworthy AI systems. By bridging theoretical foundations with practical applications, this book serves as both a scholarly reference and a catalyst for innovation in the rapidly evolving field of AI security. …
Seller Inventory # 9783031994463
- Title
- Adversarial Example Detection and Mitigation Using Machine Learning
- Author
- Ehsan Nowroozi
- Publisher
- Springer
- Publication year
- 2026
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 10
- 3031994469
- ISBN 13
- 9783031994463
- Item weight
- 656 grams
- Dimensions
- 241x160x24 mm
This book offers a comprehensive exploration of the emerging threats and defense strategies in adversarial machine learning and AI security. It covers a broad range of topics, from federated learning attacks, adversarial defenses, biometric vulnerabilities, and security weaknesses in generative AI to quantum threats and ethical considerations. It also brings together leading researchers to provide an in-depth and multifaceted perspective.
As artificial intelligence systems become increasingly integrated into critical sectors such as healthcare, finance, transportation, and national security, understanding and mitigating adversarial risks has never been more crucial. Each chapter delivers not only a detailed analysis of current challenges, but it also includes insights into practical mitigation techniques, future trends, and real-world applications.
This book is intended for researchers and graduate students working in machine learning, cybersecurity, and related disciplines. Security professionals will also find this book to be a valuable reference for understanding the latest advancements, defending against sophisticated adversarial threats, and contributing to the development of more robust, trustworthy AI systems. By bridging theoretical foundations with practical applications, this book serves as both a scholarly reference and a catalyst for innovation in the rapidly evolving field of AI security.
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About the Author
Dr. Ehsan Nowroozi is a Senior Lecturer in Cybersecurity at the University of Greenwich, UK. He holds a PhD in Information Engineering and Mathematics from the University of Siena, Italy. His research focuses on adversarial machine learning, multimedia and digital forensics, and secure federated learning. He has held academic and research positions at Ravensbourne University London, Queen’s University Belfast, Bahçeşehir University, Sabanci University, and the University of Padua. Dr. Nowroozi has co-authored numerous high-impact publications, contributed to projects like DARPA MediFor and EU’s PREMIER, and holds patents in AI-based network security. He is an Associate Editor for IEEE Transactions on Network and Service Management and actively reviews for top-tier journals. A Senior Member of IEEE and an ACM member, he teaches modules in Digital Forensics, Secure Programming, and AI for Security.
Dr. Rahim Taheri is a Senior Lecturer at the University of Portsmouth with a PhD in Computer Science and over a decade of experience in academia. His research spans secure and privacy-preserving AI, federated learning, adversarial machine learning, and AI sustainability. He has held research roles at King’s College London and the University of Padua, working with labs such as KCLIP and SPRITZ. Dr. Taheri is especially interested in developing defenses against data poisoning and adversarial threats in IoT and distributed systems. He has mentored PhD students, published in top journals and conferences, and is an active member of the IEEE (Senior Member) and ACM. His work is dedicated to exploring ethical, robust AI solutions for security challenges in modern digital infrastructures.
Dr. Lucas C. Cordeiro is a Full Professor at the University of Manchester (UoM), where he leads the Systems and Software Security (S3) Research Group. He also serves as the Business Engagement and Innovation Director and the Arm Centre of Excellence Director at UoM. Prof. Cordeiro is a globally recognized researcher in formal methods, software verification, and secure AI. He has published over 170 peer-reviewed papers and received prestigious awards, including Most Influential Paper at ASE'23 and Distinguished Paper Awards at ICSE and ASE. As the CTO of VeriBee, a UoM spinout, he drives innovation in software testing. His research funding exceeds $13M, sourced from EPSRC, Intel, Samsung, the British Council, and others. He is affiliated with the Trusted Digital Systems Cluster and postgraduate programs at the Federal University of Amazonas, Brazil.
"About the title" may belong to another edition of this title.
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