About this Book
This book is the first open-source textbook exclusively focused on Machine Learning Security and provides a comprehensive yet methodical understanding of securing today’s AI systems. It covers vulnerabilities throughout the complete machine learning life cycle from data collection, to training, and deployment and inference, as well as presents practical methods for mitigating the most harmful threats.
By integrating theoretical foundations, practical case studies, and recent research, the book covers essential topics including threat modelling, adversarial attacks, poisoning attacks, and privacy breaches.
To facilitate learning and usability, review questions to check understanding, and practical exercises to apply important concepts to practical situations are included in each chapter. This text, aimed at upper-level undergraduates and graduate students, along with computer science, cybersecurity, and AI practitioners, presumes a solid foundation in machine learning principles. The book provides readers with actionable, research-based information on the evolving security and privacy issues in artificial intelligence.
Accessibility Statement
We are actively committed to increasing the accessibility and usability of the textbooks we produce. Every attempt has been made to make this OER accessible to all learners and is compatible with assistive and adaptive technologies. We have attempted to provide closed captions, alternative text, or multiple formats for on-screen and offline access.
The web version of this resource has been designed to meet Web Content Accessibility Guidelines 2.0, level AA. In addition, it follows all guidelines in Appendix A: Checklist for Accessibility of the Accessibility Toolkit – 2nd Edition.
In addition to the web version, additional files are available in a number of file formats, including PDF, EPUB (for eReaders), and MOBI (for Kindles).
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Feedback
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