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Adversarial Machine Learning Attack Surfaces, Defence Mechanisms, Learning Theories in Artificial Intelligence /

A critical challenge in deep learning is the vulnerability of deep learning networks to security attacks from intelligent cyber adversaries. Even innocuous perturbations to the training data can be used to manipulate the behaviour of deep networks in unintended ways. In this book, we review the late...

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Bibliografiske detaljer
Main Authors: Sreevallabh Chivukula, Aneesh (Author), Yang, Xinghao (Author), Liu, Bo (Author), Liu, Wei (Author), Zhou, Wanlei (Author)
Institution som forfatter: SpringerLink (Online service)
Format: Electronisk eBog
Sprog:engelsk
Udgivet: Cham : Springer International Publishing : Imprint: Springer, 2023.
Udgivelse:1st ed. 2023.
Fag:
Artificial intelligence.
Data protection.
Artificial Intelligence.
Data and Information Security.
Online adgang:https://doi.org/10.1007/978-3-030-99772-4
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Indholdsfortegnelse:
  • Adversarial Machine Learning
  • Adversarial Deep Learning
  • Security and Privacy in Adversarial Learning
  • Game-Theoretical Attacks with Adversarial Deep Learning Models
  • Physical Attacks in the Real World
  • Adversarial Defense Mechanisms
  • Adversarial Learning for Privacy Preservation.

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