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 can be used for classroom discussions.
In addition to deep neural networks, students will also learn about
logistic regression, naïve Bayes classifiers, and support vector
machines. Written for senior undergraduate and first-year graduate
courses, the book offers a window into research methods and current
challenges. Online resources include lecture slides and image files
for instructors, and software for early course projects for
students.
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