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Adversarial Learning and Secure AI

Authors

David J. Miller
, Pennsylvania State University,
Zhen Xiang
, University of Illinois, Urbana-Champaign,
George Kesidis
, Pennsylvania State University
Published 2023

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

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