Attacks and Defenses in Robust Machine Learning

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 considerationsKey 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 landscapesIdeal 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 deploymentAttacks 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.