Chapter 1 Introduction.- Part I Foundations of Artificial Intelligence Security.- Chapter 2 Mapping the AI-Security Battlefield: Threats Across the<BR>Machine-Learning Lifecycle.- Chapter 3 Behind the Backdoors: Threats and Safeguards for Deep-Learning Systems.- Part II Backdoor Attacks and Defenses in Deep Neural Networks.- Chapter 4 Stealthy Clean-Label Backdoors: How an Image-Classification Model Can Be Attacked.- Chapter 5 Illumination-Modulated Video Backdoor Attacks on Anti-Spoofing Rebroadcast Detectors.- Chapter 6 Power Play: Backdooring DNNs Through Energy-Drain Triggers.- Chapter 7 Expecting the Next Move: Robust Backdoors under Non-IID Federated Training.- Chapter 8 When One Shield Is Not Enough: Layering Defenses Against Backdoor Attacks.- Chapter 9 Rare-Event Simulation for Black-Box Backdoor Defense.- Chapter 10 Game-Theoretic Modeling of BackdoorAttacker–Defender Dynamics.- Chapter 11 Cost-Constrained Backdoor Games in Deep Learning.- Part III DNN Watermarking for Intellectual Property Protection.- Chapter 12 Robust and Secure Watermarking for Deep Neural Networks.- Chapter 13 DNN Watermarking in Blackbox Settings using Image Mixup.- Chapter 14 Cryptographically Bound Mixup Watermarks for Black-Box DNNs.- Part IV Emerging Trends, Open Issues, and Future Research Directions in AI Security.- Chapter 15 Security Horizons: Emerging Threats and Future Directions for Trustworthy AI.- Index.