Ulteriori informazioni
Designed for upper undergraduate and graduate courses on adversarial learning and AI security, this textbook connects theory with practice using real-world examples, case studies, and hands-on student projects.
Sommario
Contents; Preface; Notation; 1. Overview of adversarial learning; 2. Deep learning background; 3. Basics of detection and mixture models; 4. Test-time evasion attacks (adversarial inputs); 5. Backdoors and before/during training defenses; 6. Post-training reverse-engineering defense (PT-RED) Against Imperceptible Backdoors; 7. Post-training reverse-engineering defense (PT-RED) against patch-incorporated backdoors; 8. Transfer post-training reverse-engineering defense (T-PT-RED) against backdoors; 9. Universal post-training backdoor defenses; 10. Test-time detection of backdoor triggers; 11. Backdoors for 3D point cloud (PC) classifiers; 12. Robust deep regression and active learning; 13. Error generic data poisoning defense; 14. Reverse-engineering attacks (REAs) on classifiers; Appendix. Support Vector Machines (SVMs); References; Index.
Info autore
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.
Riassunto
Designed for upper undergraduate and graduate courses on adversarial learning and AI security, this textbook connects theory with practice using real-world examples, case studies, and hands-on student projects.
Prefazione
The first textbook on adversarial machine learning, including both attacks and defenses, background material, and hands-on student projects.
