Enables readers to understand the full lifecycle of adversarial machine learning (AML) and how AI models can be compromised
Adversarial Machine Learning is a definitive guide to one of the most urgent challenges in artificial intelligence today: how to secure machine learning systems against adversarial threats.
This book explores the full lifecycle of adversarial machine learning (AML), providing a structured, real-world understanding of how AI models can be compromised-and what can be done about it.
The book walks readers through the different phases of the machine learning pipeline, showing how attacks emerge during training, deployment, and inference. It breaks down adversarial threats into clear categories based on attacker goals-whether to disrupt system availability, tamper with outputs, or leak private information. With clarity and technical rigor, it dissects the tools, knowledge, and access attackers need to exploit AI systems.
In addition to diagnosing threats, the book provides a robust overview of defense strategies-from adversarial training and certified defenses to privacy-preserving machine learning and risk-aware system design. Each defense is discussed alongside its limitations, trade-offs, and real-world applicability.
Readers will gain a comprehensive view of today???s most dangerous attack methods including:
Blending technical depth with practical insight, Adversarial Machine Learning equips developers, security engineers, and AI decision-makers with the knowledge they need to understand the adversarial landscape and defend their systems with confidence.
| ISBN: | 9781394402038 |
| Publication date: | 19th February 2026 |
| Author: | Jason Edwards |
| Publisher: | John Wiley & Sons, Inc. an imprint of Wiley |
| Format: | Hardback |
| Pagination: | 400 pages |
| Genres: |
Computer science |
Enables readers to understand the full lifecycle of adversarial machine learning (AML) and how AI models can be compromised
Adversarial Machine Learning is a definitive guide to one of the most urgent challenges in artificial intelligence today: how to secure machine learning systems against adversarial threats.
This book explores the full lifecycle of adversarial machine learning (AML), providing a structured, real-world understanding of how AI models can be compromised-and what can be done about it.
The book walks readers through the different phases of the machine learning pipeline, showing how attacks emerge during training, deployment, and inference. It breaks down adversarial threats into clear categories based on attacker goals-whether to disrupt system availability, tamper with outputs, or leak private information. With clarity and technical rigor, it dissects the tools, knowledge, and access attackers need to exploit AI systems.
In addition to diagnosing threats, the book provides a robust overview of defense strategies-from adversarial training and certified defenses to privacy-preserving machine learning and risk-aware system design. Each defense is discussed alongside its limitations, trade-offs, and real-world applicability.
Readers will gain a comprehensive view of today???s most dangerous attack methods including:
Blending technical depth with practical insight, Adversarial Machine Learning equips developers, security engineers, and AI decision-makers with the knowledge they need to understand the adversarial landscape and defend their systems with confidence.
Adversarial Machine Learning features in the following genres: Computer science
Adversarial Machine Learning is available in Hardback
Adversarial Machine Learning was written by Jason Edwards and published by John Wiley & Sons, Inc. an imprint of Wiley
Adversarial Machine Learning has 400 pages
£63.00