rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Adversarial example detection and mitigation using machine learning
  name:
    name part: Nowroozi, Ehsan
    role:
      role term: editor.
  name:
    name part: Taheri, Rahim
    role:
      role term: editor.
  name:
    name part: Cordeiro, Lucas
    role:
      role term: editor.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: sz
    date issued: 2026
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (405 p.).
  abstract: 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 to quantum threats and ethical considerations.
  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 Learnin…
  note: Ehsan Nowroozi, Rahim Taheri, Lucas Cordeiro, editors.
  note: Includes bibliographical references and index.
  subject:
    topic: Machine learning
    topic: Security measures
  subject:
    topic: Artificial intelligence
    topic: Security measures
  subject:
    topic: Computer security
    topic: Technological innovations
  subject:
    topic: Apprentissage automatique
    topic: Sécurité
    topic: Mesures
  subject:
    topic: Intelligence artificielle
    topic: Sécurité
    topic: Mesures
  subject:
    topic: Sécurité informatique
    topic: Innovations
  classification: Q325.5 .A28 2026
  classification: 006.31
  related item:
    title info:
      title: Adversarial Example Detection and Mitigation Using Machine Learning
    name:
      name part: Nowroozi, Ehsan
    origin info:
      publisher: Cham : Springer,c2025
  related item:
    identifier: (OCoLC)1523251020
  identifier: 9783031994470 (electronic bk.)
  identifier: 3031994477
  identifier: 3031994469
  identifier: 9783031994463
  location:
    physical location: POD
  record info:
    record content source: EBLCP
    record creation date: 260131
    record change date: 20260412195032.0
    record identifier: in00005778166
    language of cataloging:
      language term: eng