Adversarial machine learning attack di chivukula aneesh (17 risultati)
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Adversarial Machine Learning : Attack Surfaces, Defence Mechanisms, Learning Theories in Artificial Intelligence
Chivukula, Aneesh Sreevallabh; Yang, Xinghao; Liu, Bo; Liu, Wei; Zhou, Wanlei
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Da: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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Adversarial Machine Learning : Attack Surfaces, Defence Mechanisms, Learning Theories in Artificial Intelligence
Chivukula, Aneesh Sreevallabh; Yang, Xinghao; Liu, Bo; Liu, Wei; Zhou, Wanlei
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Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
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Adversarial Machine Learning: Attack Surfaces, Defence Mechanisms, Learning Theories in Artificial Intelligence
Sreevallabh Chivukula, Aneesh; Yang, Xinghao; Liu, Bo; Liu, Wei; Zhou, Wanlei
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Da: Ria Christie Collections, Uxbridge, Regno UnitoRia Christie Collections
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Adversarial Machine Learning: Attack Surfaces, Defence Mechanisms, Learning Theories in Artificial Intelligence [Hardcover] [Mar 07, 2023] Sreevallabh Chivukula, Aneesh; Yang, Xinghao; Liu, Bo; Liu, Wei and Zhou, Wanlei
Sreevallabh Chivukula, Aneesh; Yang, Xinghao; Liu, Bo; Liu, Wei; Zhou, Wanlei
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Da: StainesBook, Weybridge, SURRE, Regno UnitoStainesBook
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Condizione: New. A brand new book in pristine condition. Showing zero signs of shelf wear, creases, or damage.
Adversarial Machine Learning : Attack Surfaces, Defence Mechanisms, Learning Theories in Artificial Intelligence
Chivukula, Aneesh Sreevallabh; Yang, Xinghao; Liu, Bo; Liu, Wei; Zhou, Wanlei
- Rilegato
Da: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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Condizione: As New. Unread book in perfect condition.
Adversarial Machine Learning : Attack Surfaces, Defence Mechanisms, Learning Theories in Artificial Intelligence
Chivukula, Aneesh Sreevallabh; Yang, Xinghao; Liu, Bo; Liu, Wei; Zhou, Wanlei
- Rilegato
Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
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Condizione: As New. Unread book in perfect condition.
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Da: preigu, Osnabrück, Germaniapreigu
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Taschenbuch. Condizione: Neu. Adversarial Machine Learning | Attack Surfaces, Defence Mechanisms, Learning Theories in Artificial Intelligence | Aneesh Sreevallabh Chivukula (u. a.) | Taschenbuch | xix | Englisch | 2024 | Springer | EAN 9783030997748 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 6…9121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Adversarial Machine Learning: Attack Surfaces, Defence Mechanisms, Learning Theories in Artificial Intelligence
Sreevallabh Chivukula, Aneesh; Yang, Xinghao; Liu, Bo; Liu, Wei; Zhou, Wanlei
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Da: Books Puddle, New York, NY, U.S.A.Books Puddle
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Condizione: New. 2023rd edition NO-PA16APR2015-KAP.
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 quantificationof 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.
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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EUR 181,89
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Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 uni…ntended 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 quantificationof 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.
Adversarial Machine Learning: Attack Surfaces, Defence Mechanisms, Learning Theories in Artificial Intelligence
Sreevallabh Chivukula, Aneesh, Yang, Xinghao, Liu, Bo, Liu, Wei, Zhou, Wanlei
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Da: PAPER CAVALIER UK, London, Regno UnitoPAPER CAVALIER UK
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Adversarial Machine Learning: Attack Surfaces, Defence Mechanisms, Learning Theories in Artificial Intelligence
Chivukula, Aneesh Sreevallabh/ Yang, Xinghao/ Liu, Bo/ Liu, Wei/ Zhou, Wanlei
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Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
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Hardcover. Condizione: Brand New. 321 pages. 9.25x6.10x9.21 inches. In Stock.
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- Print on Demand
Da: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
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Condizione: new. Questo è un articolo print on demand.
Adversarial Machine Learning: Attack Surfaces, Defence Mechanisms, Learning Theories in Artificial Intelligence
Chivukula, Aneesh Sreevallabh/ Yang, Xinghao/ Liu, Bo/ Liu, Wei/ Zhou, Wanlei
- Rilegato
- Print on Demand
Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
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Hardcover. Condizione: Brand New. 321 pages. 9.25x6.10x9.21 inches. In Stock. This item is printed on demand.
Adversarial Machine Learning: Attack Surfaces, Defence Mechanisms, Learning Theories in Artificial Intelligence
Sreevallabh Chivukula, Aneesh; Yang, Xinghao; Liu, Bo; Liu, Wei; Zhou, Wanlei
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- Print on Demand
Da: Majestic Books, Hounslow, Regno UnitoMajestic Books
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Adversarial Machine Learning: Attack Surfaces, Defence Mechanisms, Learning Theories in Artificial Intelligence
Sreevallabh Chivukula, Aneesh; Yang, Xinghao; Liu, Bo; Liu, Wei; Zhou, Wanlei
- Brossura
- Print on Demand
Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
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Condizione: New. PRINT ON DEMAND.
