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ADVERSARIAL MACHINE LEARNING (SYNTHESIS LECTURES ON By Yevgeniy NEW
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Item specifics
- Condition
- Book Title
- Adversarial Machine Learning (Synthesis Lectures on Artificial
- Item Height
- 9.25 inches
- ISBN-10
- 1681733951
- ISBN
- 9781681733951
- Subject Area
- Mathematics, Computers
- Publication Name
- Adversarial Machine Learning
- Publisher
- Morgan & Claypool Publishers
- Item Length
- 9.3 in
- Subject
- Game Theory, Intelligence (Ai) & Semantics, Security / General
- Publication Year
- 2018
- Series
- Synthesis Lectures on Artificial Intelligence and Machine Learning Ser.
- Type
- Textbook
- Format
- Trade Paperback
- Language
- English
- Item Width
- 7.5 in
- Number of Pages
- 169 Pages
About this product
Product Identifiers
Publisher
Morgan & Claypool Publishers
ISBN-10
1681733951
ISBN-13
9781681733951
eBay Product ID (ePID)
26057279569
Product Key Features
Number of Pages
169 Pages
Publication Name
Adversarial Machine Learning
Language
English
Subject
Game Theory, Intelligence (Ai) & Semantics, Security / General
Publication Year
2018
Type
Textbook
Subject Area
Mathematics, Computers
Series
Synthesis Lectures on Artificial Intelligence and Machine Learning Ser.
Format
Trade Paperback
Dimensions
Item Length
9.3 in
Item Width
7.5 in
Additional Product Features
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
006.31
Table Of Content
List of Figures Preface Acknowledgments Introduction Machine Learning Preliminaries Categories of Attacks on Machine Learning Attacks at Decision Time Defending Against Decision-Time Attacks Data Poisoning Attacks Defending Against Data Poisoning Attacking and Defending Deep Learning The Road Ahead Bibliography Authors' Biographies Index
Synopsis
The field of adversarial machine learning has emerged to study vulnerabilities of machine learning approaches in adversarial settings and to develop techniques to make learning robust to adversarial manipulation. This book provides a technical overview of this field., This is a technical overview of the field of adversarial machine learning which has emerged to study vulnerabilities of machine learning approaches in adversarial settings and to develop techniques to make learning robust to adversarial manipulation. After reviewing machine learning concepts and approaches, as well as common use cases of these in adversarial settings, we present a general categorization of attacks on machine learning. We then address two major categories of attacks and associated defenses: decision-time attacks, in which an adversary changes the nature of instances seen by a learned model at the time of prediction in order to cause errors, and poisoning or training time attacks, in which the actual training dataset is maliciously modified. In our final chapter devoted to technical content, we discuss recent techniques for attacks on deep learning, as well as approaches for improving robustness of deep neural networks. We conclude with a discussion of several important issues in the area of adversarial learning that in our view warrant further research. The increasing abundance of large high-quality datasets, combined with significant technical advances over the last several decades have made machine learning into a major tool employed across a broad array of tasks including vision, language, finance, and security. However, success has been accompanied with important new challenges: many applications of machine learning are adversarial in nature. Some are adversarial because they are safety critical, such as autonomous driving. An adversary in these applications can be a malicious party aimed at causing congestion or accidents, or may even model unusual situations that expose vulnerabilities in the prediction engine. Other applications are adversarial because their task and/or the data they use are. For example, an important class of problems in security involves detection, such as malware, spam, and intrusion detection. The use of machine learning for detecting malicious entities creates an incentive among adversaries to evade detection by changing their behavior or the content of malicious objects they develop. Given the increasing interest in the area of adversarial machine learning, we hope this book provides readers with the tools necessary to successfully engage in research and practice of machine learning in adversarial settings., The increasing abundance of large high-quality datasets, combined with significant technical advances over the last several decades have made machine learning into a major tool employed across a broad array of tasks including vision, language, finance, and security. However, success has been accompanied with important new challenges: many applications of machine learning are adversarial in nature. Some are adversarial because they are safety critical, such as autonomous driving. An adversary in these applications can be a malicious party aimed at causing congestion or accidents, or may even model unusual situations that expose vulnerabilities in the prediction engine. Other applications are adversarial because their task and/or the data they use are. For example, an important class of problems in security involves detection, such as malware, spam, and intrusion detection. The use of machine learning for detecting malicious entities creates an incentive among adversaries to evade detection by changing their behavior or the content of malicius objects they develop. The field of adversarial machine learning has emerged to study vulnerabilities of machine learning approaches in adversarial settings and to develop techniques to make learning robust to adversarial manipulation. This book provides a technical overview of this field. After reviewing machine learning concepts and approaches, as well as common use cases of these in adversarial settings, we present a general categorization of attacks on machine learning. We then address two major categories of attacks and associated defenses: decision-time attacks, in which an adversary changes the nature of instances seen by a learned model at the time of prediction in order to cause errors, and poisoning or training time attacks, in which the actual training dataset is maliciously modified. In our final chapter devoted to technical content, we discuss recent techniques for attacks on deep learning, as well as approaches for improving robustness of deep neural networks. We conclude with a discussion of several important issues in the area of adversarial learning that in our view warrant further research. Given the increasing interest in the area of adversarial machine learning, we hope this book provides readers with the tools necessary to successfully engage in research and practice of machine learning in adversarial settings.
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- k***f (471)- Feedback left by buyer.Past 6 monthsVerified purchaseShipped quickly, packed with care, just as described, good value…
- s***r (12)- Feedback left by buyer.Past 6 monthsVerified purchaseProduct came in sooner than expected. Price was great and product is just as described.Lancome Monsieur Big .33 Waterproof Mascara - 01 Waterproof black NIB (#256770677366)
- a***s (575)- Feedback left by buyer.Past 6 monthsVerified purchaseSent promptly, well-packaged & as described. Thanks.hp 65XL Black Ink Cartridge Used Only OneTime (Reason Change printer) (#256542816547)