Adversarial Learning and Secure AI
- Textbook
Description
Providing a logical framework for student learning, this is the first textbook on adversarial learning. It introduces vulnerabilities of deep learning, then demonstrates methods for defending against attacks and making AI generally more robust. To help students connect theory with practice, it explains and evaluates attack-and-defense scenarios alongside real-world examples. Feasible, hands-on student projects, which increase in difficulty throughout the book, give students practical experience and help to improve their Python and PyTorch skills. Book chapters conclude with questions that…
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Key features
- Connects theory with practice by featuring realistic examples, case studies, and hands-on student projects in each chapter
- Offers instructors several options for structuring a course on adversarial machine learning, including the necessary background material
- Presents a logical structure to assist student learning
- Strengthens critical thinking by evaluating attacks and defenses
- Online resources include image files and lecture slides for instructors, and software for early course projects that improves students' skills in Python and PyTorch
- Chapters conclude with a set of questions that are suitable for classroom discussion
About the book
- DOI https://doi.org/10.1017/9781009315647
- Subjects Computer Science,Machine Learning and Pattern Recognition,Security, Cryptography, and Privacy
- Format: Hardback
- Publication date: 31 August 2023
- ISBN: 9781009315678
- Dimensions (mm): 244 x 170 mm
- Weight: 0.86kg
- Page extent: 350 pages
- Availability: In stock
- Format: Digital
- Publication date: 07 September 2023
- ISBN: 9781009315647
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