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Attacks and Defenses in Robust Machine Learning: Adversarial AI Techniques
Paperback
General ComputersComputer Security
Publisher: Independently Published
Published: Jun 8 2025
Pages: 408
Weight: 1.08
Height: 1.01 Width: 6.00 Depth: 9.00
Language: English
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.
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