This study allows readers to get to grips with the conceptual tools and practical techniques for building robust machine learning in the face of adversaries.
The increasing abundance of large high-quality datasets, combined with significant technical advances over the last several decades have made machine learning i
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simp
This is a technical overview of the field of adversarial machine learning which has emerged to study vulnerabilities of machine learning approaches in adversari
Adversarial Robustness for Machine Learning summarizes the recent progress on this topic and introduces popular algorithms on adversarial attack, defense and ve