Attacks and Defenses in Robust Machine Learning: Adversarial AI Techniques

By Maria Johnsen

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 taxonomiesMajor attack types: evasion attacks (including gradient-based methods like FGSM and PGD-style perturbations), poisoning attacks, backdoor/trojan injection, and model manipulationDefense techniques: adversarial training, defensive distillation, input transformations, certified robustness, and robust architecturesDomain-specific risks in computer vision, natural language processing (NLP), healthcare AI, finance, and autonomous systemsReal-world case studies demonstrating system vulnerabilities and mitigation strategiesMathematical foundations supporting robust ML designEmerging threats, privacy risks, and regulatory and legal considerations

Key Features:

End-to-end coverage of adversarial attacks and defense mechanismsPractical insights for securing production machine learning systemsCross-industry applications and risk mitigation strategiesForward-looking analysis of AI safety, governance, and future threat landscapes

Ideal For:

Machine learning engineers building production-grade AI systemsCybersecurity professionals focused on AI and model securityGraduate students and researchers in adversarial machine learningAI 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.

• Format: Paperback
• Language: English
• ISBN: B0FCFKDNKL
• ISBN13: 9798287319298
• Release Date: June 2025
• Publisher: Independently Published
• Length: 408 Pages
• Weight: 1.08 lbs.
• Dimensions: 1.0" x 6.0" x 9.0"