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.

Aishan Liu, Associate Professor in the State Key Laboratory of Complex & Critical Software Environment, Department of Computer Science and Engineering at Beihang University. His research interestes are centered around AI Security and Security, with broad interests in the areas of Adversarial Examples, Backdoor Attacks, Interpretable Deep Learning, Model Robustness, Fairness Testing, AI Testing and Evaluation, and their applications in real-world scenarios.

Yuanfang Guo received the BEng degree in computer engineering and the PhD degree in electronic and computer engineering from the Hong Kong University of Science and Technology, Hong Kong, in 2009 and 2015, respectively. He is currently an associate professor with the School of Computer Science and Engineering, Beihang University, Beijing, China. His current research interests include multimedia security, artificial intelligence security, and graph neural networks. He has published over 90 scientific papers in international journals and conferences and received the best paper award in PRCV 2022. He is currently serving as an associate editor for IEEE Signal Processing Letters and IET Image Processing.

Jiakai Wang, Research Scientist, Associate researcher, in Zhongguancun Laboratory, Beijing, China. He received the Ph.D. degree in 2022 from Beihang University. His research interest is Trustworthy AI in Multimodal, which consists of the physical adversarial example’s generation, adversarial defense and evaluation.

Xianglong Liu, full Professor in School of Computer Science and Engineering at Beihang University. His research interests include fast visual computing (e.g., large-scale search/understanding) and robust deep learning (e.g., network quantization, adversarial attack/defense, few shot learning). He has published more than 100 scientific papers in international journals and conferences. He received NSFC Excellent Young Scientists Fund, and was selected into 2019 Beijing Nova Program, MSRA StarTrack Program, and 2015 CCF Young Talents Development Program.

Yunhong Wang is currently a professor with the School of Computer Science and Engineering, Beihang University, Beijing, China. Her current research interests include biometrics, pattern recognition, computer vision, data fusion, and image processing. She has been awarded IEEE Fellow for her contributions to iris recognition and face recognition and has been awarded IAPR Fellow for her contributions to pattern recognition and biometrics. She has published more than 300 scientific papers in international journals and conferences. She has served on the editorial board of IEEE Transactions on Dependable and Secure Computing, IEEE Transactions on Biometrics, Behavior, and Identity Sciences, and Pattern Recognition.

Dacheng Tao is currently a Distinguished University Professor in the College of Computing & Data Science at Nanyang Technological University. He mainly applies statistics and mathematics to artificial intelligence and data science, and his research is detailed in one monograph and over 200 publications in prestigious journals and proceedings at leading conferences, with best paper awards, best student paper awards, and test-of-time awards. His publications have been cited over 112K times and he has an h-index 160+ in Google Scholar. He received the 2015 and 2020 Australian Eureka Prize, the 2018 IEEE ICDM Research Contributions Award, and the 2021 IEEE Computer Society McCluskey Technical Achievement Award. He is a Fellow of the Australian Academy of Science, AAAS, ACM and IEEE.