Adversarial Learning and Secure AI

ISBN-13: 9781009315678 / Twarda / 2023 / 350 str.

George (Pennsylvania State University) Kesidis

Opis

The first textbook on adversarial machine learning, including both attacks and defenses, background material, and hands-on student projects.

Recenzje

'In a field that is moving at break-neck speed, this book provides a strong foundation for anyone interested in joining the fray.' Amir Rahmati, Stony Brook

Spis treści

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.