Adversarial Robustness For Machine Learning

Explore the critical field of adversarial robustness in machine learning with this comprehensive guide. Delve into the vulnerabilities of machine learning models to adversarial attacks and discover effective defense strategies. This book offers a structured approach to understanding the landscape of adversarial robustness, covering theoretical foundations, practical implementations, and cutting-edge research. Learn about various attack techniques, including gradient-based methods and optimization-based approaches, that can fool even the most sophisticated models. Discover a range of defense mechanisms, from adversarial training and input preprocessing to certified robustness techniques. Gain insights into evaluating the robustness of machine learning systems and designing models that are resilient to adversarial manipulation. This book is an invaluable resource for researchers, practitioners, and students seeking to develop robust and reliable machine learning solutions in the face of adversarial threats.