Adversarial Machine Learning
DOWNLOAD
Download Adversarial Machine Learning PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Adversarial Machine Learning book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Adversarial Machine Learning
DOWNLOAD
Author : Yevgeniy Vorobeychik
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2018-08-08
Adversarial Machine Learning written by Yevgeniy Vorobeychik and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-08 with Computers categories.
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.
Adversarial Robustness For Machine Learning
DOWNLOAD
Author : Pin-Yu Chen
language : en
Publisher: Academic Press
Release Date : 2022-08-20
Adversarial Robustness For Machine Learning written by Pin-Yu Chen and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-20 with Computers categories.
Adversarial Robustness for Machine Learning summarizes the recent progress on this topic and introduces popular algorithms on adversarial attack, defense and verification. Sections cover adversarial attack, verification and defense, mainly focusing on image classification applications which are the standard benchmark considered in the adversarial robustness community. Other sections discuss adversarial examples beyond image classification, other threat models beyond testing time attack, and applications on adversarial robustness. For researchers, this book provides a thorough literature review that summarizes latest progress in the area, which can be a good reference for conducting future research. In addition, the book can also be used as a textbook for graduate courses on adversarial robustness or trustworthy machine learning. While machine learning (ML) algorithms have achieved remarkable performance in many applications, recent studies have demonstrated their lack of robustness against adversarial disturbance. The lack of robustness brings security concerns in ML models for real applications such as self-driving cars, robotics controls and healthcare systems. - Summarizes the whole field of adversarial robustness for Machine learning models - Provides a clearly explained, self-contained reference - Introduces formulations, algorithms and intuitions - Includes applications based on adversarial robustness
Adversarial Machine Learning
DOWNLOAD
Author : Aneesh Sreevallabh Chivukula
language : en
Publisher: Springer Nature
Release Date : 2023-03-06
Adversarial Machine Learning written by Aneesh Sreevallabh Chivukula and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-06 with Computers categories.
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.
Adversarial Machine Learning
DOWNLOAD
Author : Anthony D. Joseph
language : en
Publisher: Cambridge University Press
Release Date : 2019-02-21
Adversarial Machine Learning written by Anthony D. Joseph and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-21 with Computers categories.
This study allows readers to get to grips with the conceptual tools and practical techniques for building robust machine learning in the face of adversaries.
Robust Machine Learning Algorithms And Systems For Detection And Mitigation Of Adversarial Attacks And Anomalies
DOWNLOAD
Author : National Academies of Sciences, Engineering, and Medicine
language : en
Publisher: National Academies Press
Release Date : 2019-08-22
Robust Machine Learning Algorithms And Systems For Detection And Mitigation Of Adversarial Attacks And Anomalies written by National Academies of Sciences, Engineering, and Medicine and has been published by National Academies Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-22 with Computers categories.
The Intelligence Community Studies Board (ICSB) of the National Academies of Sciences, Engineering, and Medicine convened a workshop on December 11â€"12, 2018, in Berkeley, California, to discuss robust machine learning algorithms and systems for the detection and mitigation of adversarial attacks and anomalies. This publication summarizes the presentations and discussions from the workshop.
Challenges And Solutions For Cybersecurity And Adversarial Machine Learning
DOWNLOAD
Author : Ul Rehman, Shafiq
language : en
Publisher: IGI Global
Release Date : 2025-06-06
Challenges And Solutions For Cybersecurity And Adversarial Machine Learning written by Ul Rehman, Shafiq and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-06 with Computers categories.
Adversarial machine learning poses a threat to cybersecurity by exploiting vulnerabilities in AI models through manipulated inputs. These attacks can cause systems in healthcare, finance, and autonomous vehicles to make dangerous or misleading decisions. A major challenge lies in detecting these small issues and defending learning models and organizational data without sacrificing performance. Ongoing research and cross-sector collaboration are essential to develop robust, ethical, and secure machine learning systems. Further research may reveal better solutions to converge cyber technology, security, and machine learning tools. Challenges and Solutions for Cybersecurity and Adversarial Machine Learning explores adversarial machine learning and deep learning within cybersecurity. It examines foundational knowledge, highlights vulnerabilities and threats, and proposes cutting-edge solutions to counteract adversarial attacks on AI systems. This book covers topics such as data privacy, federated learning, and threat detection, and is a useful resource for business owners, computer engineers, security professionals, academicians, researchers, and data scientists.
Cyber Security And Adversarial Machine Learning
DOWNLOAD
Author : Ferhat Ozgur Catak
language : en
Publisher:
Release Date : 2021-10-30
Cyber Security And Adversarial Machine Learning written by Ferhat Ozgur Catak and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-30 with categories.
Focuses on learning vulnerabilities and cyber security. The book gives detail on the new threats and mitigation methods in the cyber security domain, and provides information on the new threats in new technologies such as vulnerabilities in deep learning, data privacy problems with GDPR, and new solutions.
Adversarial Learning And Secure Ai
DOWNLOAD
Author : David J. Miller
language : en
Publisher: Cambridge University Press
Release Date : 2023-08-31
Adversarial Learning And Secure Ai written by David J. Miller and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-31 with Computers categories.
Providing a logical framework for student learning, this is the first textbook on adversarial learning. It introduces vulnerabilities of deep learning, then demonstrates methods for defending against attacks and making AI generally more robust. To help students connect theory with practice, it explains and evaluates attack-and-defense scenarios alongside real-world examples. Feasible, hands-on student projects, which increase in difficulty throughout the book, give students practical experience and help to improve their Python and PyTorch skills. Book chapters conclude with questions that can be used for classroom discussions. In addition to deep neural networks, students will also learn about logistic regression, naïve Bayes classifiers, and support vector machines. Written for senior undergraduate and first-year graduate courses, the book offers a window into research methods and current challenges. Online resources include lecture slides and image files for instructors, and software for early course projects for students.
Adversarial Machine Learning
DOWNLOAD
Author : Jason Edwards
language : en
Publisher: John Wiley & Sons
Release Date : 2026-01-06
Adversarial Machine Learning written by Jason Edwards and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2026-01-06 with Computers categories.
Enables readers to understand the full lifecycle of adversarial machine learning (AML) and how AI models can be compromised Adversarial Machine Learning is a definitive guide to one of the most urgent challenges in artificial intelligence today: how to secure machine learning systems against adversarial threats. This book explores the full lifecycle of adversarial machine learning (AML), providing a structured, real-world understanding of how AI models can be compromised—and what can be done about it. The book walks readers through the different phases of the machine learning pipeline, showing how attacks emerge during training, deployment, and inference. It breaks down adversarial threats into clear categories based on attacker goals—whether to disrupt system availability, tamper with outputs, or leak private information. With clarity and technical rigor, it dissects the tools, knowledge, and access attackers need to exploit AI systems. In addition to diagnosing threats, the book provides a robust overview of defense strategies—from adversarial training and certified defenses to privacy-preserving machine learning and risk-aware system design. Each defense is discussed alongside its limitations, trade-offs, and real-world applicability. Readers will gain a comprehensive view of today???s most dangerous attack methods including: Evasion attacks that manipulate inputs to deceive AI predictions Poisoning attacks that corrupt training data or model updates Backdoor and trojan attacks that embed malicious triggers Privacy attacks that reveal sensitive data through model interaction and prompt injection Generative AI attacks that exploit the new wave of large language models Blending technical depth with practical insight, Adversarial Machine Learning equips developers, security engineers, and AI decision-makers with the knowledge they need to understand the adversarial landscape and defend their systems with confidence.
Adversarial Example Detection And Mitigation Using Machine Learning
DOWNLOAD
Author : Ehsan Nowroozi
language : en
Publisher: Springer
Release Date : 2025-10-06
Adversarial Example Detection And Mitigation Using Machine Learning written by Ehsan Nowroozi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-10-06 with Computers categories.
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. It also brings together leading researchers to provide an in-depth and multifaceted perspective. As artificial intelligence systems become increasingly integrated into critical sectors such as healthcare, finance, transportation, and national security, understanding and mitigating adversarial risks has never been more crucial. Each chapter delivers not only a detailed analysis of current challenges, but it also includes insights into practical mitigation techniques, future trends, and real-world applications. This book is intended for researchers and graduate students working in machine learning, cybersecurity, and related disciplines. Security professionals will also find this book to be a valuable reference for understanding the latest advancements, defending against sophisticated adversarial threats, and contributing to the development of more robust, trustworthy AI systems. By bridging theoretical foundations with practical applications, this book serves as both a scholarly reference and a catalyst for innovation in the rapidly evolving field of AI security.