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Paperback Attacks and Defenses in Robust Machine Learning: Adversarial AI Techniques Book

ISBN: B0FCFKDNKL

ISBN13: 9798287319298

Attacks and Defenses in Robust Machine Learning: Adversarial AI Techniques

Attacks and Defenses in Robust Machine Learning is a comprehensive, authoritative guide to adversarial machine learning, AI security, and robust model design. It explains how modern machine learning systems can be attacked and how to defend them across real-world applications and high-risk domains.

Designed for ML engineers, cybersecurity professionals, AI researchers, data scientists, and policy makers, this book bridges theory and practice to help readers build secure, resilient, and trustworthy AI systems.

Spanning 30 structured chapters, it delivers a complete deep dive into adversarial ML, including:

Core adversarial machine learning theory and attack taxonomies

Major attack types: evasion attacks, poisoning attacks, backdoors, and model manipulation

Defense techniques: adversarial training, defensive distillation, input transformations, and robust architectures

Domain-specific risks in computer vision, natural language processing (NLP), healthcare AI, finance, and autonomous systems

Real-world case studies demonstrating system vulnerabilities and mitigation strategies

Mathematical foundations supporting robust ML design

Emerging threats, privacy risks, and regulatory and legal considerations

Key Features:

End-to-end coverage of adversarial attacks and defense mechanisms

Practical insights for securing production machine learning systems

Cross-industry applications and risk mitigation strategies

Forward-looking analysis of AI safety, governance, and future threat landscapes

Ideal For:

Machine learning engineers building production-grade AI systems

Cybersecurity professionals focused on AI and model security

Graduate students and researchers in adversarial machine learning

AI policy leaders and technical decision-makers shaping safe AI deployment

Attacks and Defenses in Robust Machine Learning is an essential reference for anyone seeking to understand, evaluate, and secure machine learning systems in today's increasingly adversarial AI landscape.

Recommended

Format: Paperback

Condition: New

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