Adversarial machine learning : attack surfaces, defence mechanisms, learning theories in artificial intelligence / Aneesh Sreevallabh Chivukula, Xinghao Yang, Bo Liu, Wei Liu, Wanlei Zhou.
Chivukula, Aneesh Sreevallabh, author.; Yang, Xinghao, author.; Liu, Bo, author.; Liu, Wei (Chemical engineer), author.; Zhou, Wanlei, author.
2023
QA76.9.A25 C55 2023
Available Online

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Title Adversarial machine learning : attack surfaces, defence mechanisms, learning theories in artificial intelligence / Aneesh Sreevallabh Chivukula, Xinghao Yang, Bo Liu, Wei Liu, Wanlei Zhou.
Author Chivukula, Aneesh Sreevallabh, author.
ISBN 9783030997724 electronic book
3030997723 electronic book
9783030997717
3030997715
DOI https://doi.org/10.1007/978-3-030-99772-4
Published Cham, Switzerland : Springer, [2023]
Language English
Description 1 online resource (1 volume)
Other Standard Identifiers 10.1007/978-3-030-99772-4 doi
Call Number QA76.9.A25 C55 2023
Dewey Decimal Classification 005.8
Summary 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 latest developments in adversarial attack technologies in computer vision; natural language processing; and cybersecurity with regard to multidimensional, textual and image data, sequence data, and temporal data. In turn, we assess the robustness properties of deep learning networks to produce a taxonomy of adversarial examples that characterises the security of learning systems using game theoretical adversarial deep learning algorithms. The state-of-the-art in adversarial perturbation-based privacy protection mechanisms is also reviewed. We propose new adversary types for game theoretical objectives in non-stationary computational learning environments. Proper quantification of the hypothesis set in the decision problems of our research leads to various functional problems, oracular problems, sampling tasks, and optimization problems. We also address the defence mechanisms currently available for deep learning models deployed in real-world environments. The learning theories used in these defence mechanisms concern data representations, feature manipulations, misclassifications costs, sensitivity landscapes, distributional robustness, and complexity classes of the adversarial deep learning algorithms and their applications. In closing, we propose future research directions in adversarial deep learning applications for resilient learning system design and review formalized learning assumptions concerning the attack surfaces and robustness characteristics of artificial intelligence applications so as to deconstruct the contemporary adversarial deep learning designs. Given its scope, the book will be of interest to Adversarial Machine Learning practitioners and Adversarial Artificial Intelligence researchers whose work involves the design and application of Adversarial Deep Learning.
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Source of Description Description based on online resource; title from digital title page (viewed on April 27, 2023).
Added Author Yang, Xinghao, author.
Liu, Bo, author.
Liu, Wei (Chemical engineer), author.
Zhou, Wanlei, author.
Available in Other Form Adversarial deep learning in cybersecurity.
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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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