LEADER 05777nam 22006255 450 001 9911061849303321 005 20260122120405.0 010 $a3-031-99447-7 024 7 $a10.1007/978-3-031-99447-0 035 $a(CKB)45004206600041 035 $a(MiAaPQ)EBC32510905 035 $a(Au-PeEL)EBL32510905 035 $a(DE-He213)978-3-031-99447-0 035 $a(EXLCZ)9945004206600041 100 $a20260122d2026 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdversarial Example Detection and Mitigation Using Machine Learning /$fedited by Ehsan Nowroozi, Rahim Taheri, Lucas Cordeiro 205 $a1st ed. 2026. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2026. 215 $a1 online resource (405 pages) 225 1 $aComputer Science Series 311 08$a3-031-99446-9 327 $aPreface -- Part I Foundations of Adversarial Machine Learning -- Chapter 1 A Brief Survey of Emerging Threats to AI Security -- Chapter 2 Ethical Considerations and Regulatory Standards for Adversarial Defense -- Chapter 3 Vulnerability Detection: From Formal Verification to Large Language Models and Hybrid Approaches: A Comprehensive Overview -- Part II Attacks on AI Systems -- Chapter 4 Backdoor Attacks in Text Classification: Threats, Methods, and Emerging Challenges -- Chapter 5 Biometric Template-Based Reconstruction Attack in Machine Learning -- Chapter 6 Security Weaknesses of Code Generated by Generative AI -- Chapter 7 No More Paper Tigers: A Taxonomy of Realistic Adversarial Attacks on Machine Learning based Malware Detection -- Chapter 8 Adversarial Threats to Digital Twin Technology: A Taxonomy of Vulnerabilities and Attack Surfaces -- Chapter 9 Quantum Adversarial Artificial Intelligence in Secure Internet of Things Networks -- Part III Defense Techniques and Robustness Strategies -- Chapter 10 Detecting and Mitigating Adversarial Examples in Neural Networks: An Enhanced PGD Approach -- Chapter 11 The Role of Explainable AI (XAI) in Enhancing the Security of Machine Learning Systems Against Adversarial Attacks -- Chapter 12 Neurodevelopmental-Inspired Training Enhances Adversarial Robustness of a Primary Visual Cortex-Based Model -- Chapter 13 Evaluating and Defending Against Adversarial Attacks on LLM-Generated LSTM Models -- Chapter 14 Statistical Feature-Based Detection of Adversarial Noise and Patch Attacks in Image and Deepfake Analysis -- Chapter 15 Probabilistic Robustness in Deep Learning: A Concise yet Comprehensive Guide -- Part IV Federated Learning under Attack and Defense -- Chapter 16 Enhancing Federated Learning Security: Cluster-Based Strategies to Counter GAN-Poisoned Attacks -- Chapter 17 Defense Strategies in Federated Learning Against Adversarial Attacks -- Chapter 18 Dual Perspectives on GAN-Based Data Poisoning in Federated Learning: VagueGAN Attacks and Data Poisoning Detection -- Part V Applications and Case Studies -- Chapter 19 Cyber Risk Assessment in IT/OT Convergence using Machine Learning -- Chapter 20 Anomaly Detection Techniques in IoT Networks: Review and Comparative Analysis -- Chapter 21 Bridging the Gap from Research to Reality: Methods for Fortifying Mitigation Measures against Adversarial AI -- Index. 330 $aThis book offers a comprehensive exploration of the emerging threats and defense strategies in adversarial machine learning and AI security. It covers a broad range of topics, from federated learning attacks, adversarial defenses, biometric vulnerabilities, and security weaknesses in generative AI to quantum threats and ethical considerations. It also brings together leading researchers to provide an in-depth and multifaceted perspective. As artificial intelligence systems become increasingly integrated into critical sectors such as healthcare, finance, transportation, and national security, understanding and mitigating adversarial risks has never been more crucial. Each chapter delivers not only a detailed analysis of current challenges, but it also includes insights into practical mitigation techniques, future trends, and real-world applications. This book is intended for researchers and graduate students working in machine learning, cybersecurity, and related disciplines. Security professionals will also find this book to be a valuable reference for understanding the latest advancements, defending against sophisticated adversarial threats, and contributing to the development of more robust, trustworthy AI systems. By bridging theoretical foundations with practical applications, this book serves as both a scholarly reference and a catalyst for innovation in the rapidly evolving field of AI security. 410 0$aComputer Science Series 606 $aArtificial intelligence 606 $aMachine learning 606 $aData protection$xLaw and legislation 606 $aCooperating objects (Computer systems) 606 $aArtificial Intelligence 606 $aMachine Learning 606 $aPrivacy 606 $aCyber-Physical Systems 615 0$aArtificial intelligence. 615 0$aMachine learning. 615 0$aData protection$xLaw and legislation. 615 0$aCooperating objects (Computer systems) 615 14$aArtificial Intelligence. 615 24$aMachine Learning. 615 24$aPrivacy. 615 24$aCyber-Physical Systems. 676 $a006.31 700 $aNowroozi$b Ehsan$01893119 701 $aNowroozi$01893120 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911061849303321 996 $aAdversarial Example Detection and Mitigation Using Machine Learning$94540600 997 $aUNINA