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Adversarial example detection and mitigation using machine learning /

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This 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 t...

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Bibliographic Details
Other Authors: Nowroozi, Ehsan (Editor), Taheri, Rahim (Editor), Cordeiro, Lucas (Editor)
Format: eBook
Language:English
Published: Cham : Springer, [2026]
Subjects:
Machine learning > Security measures.
Artificial intelligence > Security measures.
Computer security > Technological innovations.
Apprentissage automatique > Sécurité > Mesures.
Intelligence artificielle > Sécurité > Mesures.
Sécurité informatique > Innovations.
Table of Contents:
  • Preface
  • 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.