Adversarial Learning and Secure AI - Couverture rigide

Miller, David J.; Xiang, Zhen; Kesidis, George

Couverture rigide

ISBN 10 : 1009315676 ISBN 13 : 9781009315678

Editeur : Cambridge University Press, 2023

Synopsis

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.

A propos du livre

À propos des auteurs

David J. Miller is Professor of Electrical Engineering at the Pennsylvania State University.

Zhen Xiang is a post-doctoral research associate in Computer Science at the University of Illinois, Urbana-Champaign.

George Kesidis is Professor of Computer Science and Engineering, and of Electrical Engineering, at the Pennsylvania State University.

Détails de l'édition

• Éditeur: Cambridge University Press
• Date d'édition: 2023
• Langue: anglais
• ISBN 10: 1009315676
• ISBN 13: 9781009315678
• Reliure: Relié
• Nombre de pages: 350
• Coordonnées du fabricant: non disponible
• Personne responsable: Avery; https://www.avery.fr/; ; chemin des peupliers 27; Dardilly; France