About this book:

Adversarial Machine Learning Explained provides a comprehensive guide to securing machine learning systems against malicious manipulation. It begins by establishing why adversarial ML matters, tracing the history of attacks from early spam‑filter evasion to modern deep‑learning vulnerabilities, and outlines the high stakes in sectors such as autonomous vehicles, healthcare, finance, and cybersecurity. The book then lays the necessary ML foundations—features, labels, training versus inference, loss functions, optimizers, generalization, and neural‑network mechanics—so that security practitioners can understand how models learn and where they can be subverted.

From this base, it introduces a structured threat‑modeling process tailored to ML pipelines and presents a taxonomy of attacks: evasion (fooling models at inference time), poisoning (corrupting training data or the learning process, including backdoors and Trojans), and privacy (extracting sensitive data via model inversion, membership inference, or model stealing). Each attack class is examined in depth, covering white‑box and black‑box methods, gradient‑based techniques (FGSM, PGD, C&W), query‑efficient and transfer attacks, physical‑world robustness (adversarial patches, audio Trojans), and emerging vectors such as prompt injection, jailbreaking, and model theft in large language models.

The defensive side emphasizes a defense‑in‑depth strategy: robust training (adversarial training, gradient regularization), input filtering and anomaly detection, certified and provable robustness (interval bound propagation, randomized smoothing), protections against poisoning and backdoors (data provenance, Neural Cleanse, robust aggregation in federated learning), privacy‑preserving ML (differential privacy, federated learning, trusted execution environments), secure data and feature‑engineering pipelines, hardened MLOps (secure CI/CD, versioned model registries, deployment patterns, API security, secrets management), continuous monitoring and incident response, red‑team evaluation, and governance, risk, and compliance frameworks (NIST AI RMF, EU AI Act, GDPR). The book concludes with domain‑specific case studies for vision, NLP, and recommendation systems, and a practical roadmap for building secure ML engineering programs that integrate threat modeling, robust model development, operational safeguards, and ongoing validation to keep AI trustworthy in the face of evolving adversarial threats.

What You'll Find Inside:

• Comprehensive overview of adversarial ML threats including evasion attacks, data poisoning, backdoor implants, and privacy breaches like model inversion.
• In-depth analysis of attack techniques such as FGSM, PGD, and black-box methods, alongside defenses like adversarial training and certified robustness.
• Practical strategies for secure MLOps, covering build, deploy, and runtime controls, alongside threat modeling and red teaming methodologies.
• Privacy-preserving techniques including differential privacy, federated learning, and secure data pipelines to protect sensitive training data.
• Exploration of emerging risks in large language models, including prompt injection, jailbreaking, model stealing, and supply chain vulnerabilities.

Who's It For:

This book is essential for machine learning engineers, security professionals, data scientists, MLOps practitioners, and technology leaders responsible for deploying AI systems in production environments. It is particularly valuable for those working in cybersecurity, healthcare, finance, autonomous systems, or any domain where ML model integrity, privacy, and robustness are critical business and safety requirements.