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