Introduction to Artificial Intelligence Security

Beschreibung

This book provides a comprehensive and systematic exploration of the principles, theoretical frameworks, and practical applications of AI security. Spanning four parts and twelve chapters, it offers readers an in-depth understanding of the challenges and solutions associated with the safe and responsible development of artificial intelligence systems. Part One (Chapters 1–2) delves into the historical development of AI, examining the security challenges that have arisen with its rapid advancement. These chapters lay the groundwork by covering fundamental AI concepts, including machine learning and deep learning. Part Two (Chapters 3–5) explores the inherent risks of AI systems, often referred to as endogenous security issues. By analyzing the lifecycle of AI systems, this section addresses critical vulnerabilities such as adversarial attacks, privacy breaches, and stability concerns. Part Three (Chapters 6–9) focuses on derivative security issues arising from the broader implications of AI deployment. This section provides an in-depth discussion of content-related risks, including editorial and generative content security, as well as challenges tied to decision-making integrity. Part Four (Chapters 10–12) addresses additional security considerations and highlights best practices for ensuring the responsible use of intelligent applications. These chapters, in conjunction with the earlier sections, form a cohesive framework for understanding AI security. Chapter 12 concludes the book with a synthesis of key insights and offers a forward-looking perspective on the future of AI security. Additionally, the appendix compiles a curated list of resources for further research on AI security, equipping readers with tools to explore the subject more deeply. This book is designed for a diverse audience, including senior undergraduate and graduate students in computer science, AI, and cybersecurity programs, as well as researchers and scholars in related fields.

This book provides a comprehensive and systematic exploration of the principles, theoretical frameworks, and practical applications of AI security. Spanning four parts and twelve chapters, it offers readers an in-depth understanding of the challenges and solutions associated with the safe and responsible development of artificial intelligence systems.

Part One (Chapters 1–2) delves into the historical development of AI, examining the security challenges that have arisen with its rapid advancement. These chapters lay the groundwork by covering fundamental AI concepts, including machine learning and deep learning.

Part Two (Chapters 3–5) explores the inherent risks of AI systems, often referred to as endogenous security issues. By analyzing the lifecycle of AI systems, this section addresses critical vulnerabilities such as adversarial attacks, privacy breaches, and stability concerns.

Part Three (Chapters 6–9) focuses on derivative security issues arising from the broader implications of AI deployment. This section provides an in-depth discussion of content-related risks, including editorial and generative content security, as well as challenges tied to decision-making integrity.

Part Four (Chapters 10–12) addresses additional security considerations and highlights best practices for ensuring the responsible use of intelligent applications. These chapters, in conjunction with the earlier sections, form a cohesive framework for understanding AI security. Chapter 12 concludes the book with a synthesis of key insights and offers a forward-looking perspective on the future of AI security.

Additionally, the appendix compiles a curated list of resources for further research on AI security, equipping readers with tools to explore the subject more deeply.

This book is designed for a diverse audience, including senior undergraduate and graduate students in computer science, AI, and cybersecurity programs, as well as researchers and scholars in related fields.

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Aishan Liu

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AI Security AI Risk Management AI Privacy Protection Machine Learning Security Deep Learning Security AI security framework

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