Artificial Intelligence Data and Model Safety: Risks, Attacks and Defenses begins with a brief review of the history of AI and AI security and then introduces the fundamental aspects of machine learning and AI security. Two key aspects are covered: data security and modelling. It provides detailed explanations of a wide range of attacks and defense algorithms related to data security, as well as adversarial attack/defense, backdoor attack/defense, and extraction attack/defense algorithms related to model security. By providing a systematic, comprehensive, and in-depth introduction to the topic, this book help readers understand the advanced attack and defense techniques in the field of AI security. - Systematic: comprehensively introduces AI safety, covering both attack and defense technologies - In-depth: covers a broad range of attack and defense strategies from the perspectives of adversarial learning and robust optimization, providing detailed explanations and insights - Includes the latest research developments and state-of-the-art techniques in the field of AI security