Adversarial Robustness For Machine Learning
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Adversarial Robustness For Machine Learning
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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 veri?cation. Sections cover adversarial attack, veri?cation and defense, mainly focusing on image classi?cation 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
Machine Learning Algorithms
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Author : Fuwei Li
language : en
Publisher: Springer Nature
Release Date : 2022-11-14
Machine Learning Algorithms written by Fuwei Li and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-14 with Computers categories.
This book demonstrates the optimal adversarial attacks against several important signal processing algorithms. Through presenting the optimal attacks in wireless sensor networks, array signal processing, principal component analysis, etc, the authors reveal the robustness of the signal processing algorithms against adversarial attacks. Since data quality is crucial in signal processing, the adversary that can poison the data will be a significant threat to signal processing. Therefore, it is necessary and urgent to investigate the behavior of machine learning algorithms in signal processing under adversarial attacks. The authors in this book mainly examine the adversarial robustness of three commonly used machine learning algorithms in signal processing respectively: linear regression, LASSO-based feature selection, and principal component analysis (PCA). As to linear regression, the authors derive the optimal poisoning data sample and the optimal feature modifications, and also demonstrate the effectiveness of the attack against a wireless distributed learning system. The authors further extend the linear regression to LASSO-based feature selection and study the best strategy to mislead the learning system to select the wrong features. The authors find the optimal attack strategy by solving a bi-level optimization problem and also illustrate how this attack influences array signal processing and weather data analysis. In the end, the authors consider the adversarial robustness of the subspace learning problem. The authors examine the optimal modification strategy under the energy constraints to delude the PCA-based subspace learning algorithm. This book targets researchers working in machine learning, electronic information, and information theory as well as advanced-level students studying these subjects. R&D engineers who are working in machine learning, adversarial machine learning, robust machine learning, and technical consultants working on the security and robustness of machine learning are likely to purchase this book as a reference guide.
Robust Machine Learning Algorithms And Systems For Detection And Mitigation Of Adversarial Attacks And Anomalies
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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.
Adversarial Robustness Of Deep Learning Models
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Author : Samarth Gupta (S.M.)
language : en
Publisher:
Release Date : 2020
Adversarial Robustness Of Deep Learning Models written by Samarth Gupta (S.M.) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Efficient operation and control of modern day urban systems such as transportation networks is now more important than ever due to huge societal benefits. Low cost network-wide sensors generate large amounts of data which needs to processed to extract useful information necessary for operational maintenance and to perform real-time control. Modern Machine Learning (ML) systems, particularly Deep Neural Networks (DNNs), provide a scalable solution to the problem of information retrieval from sensor data. Therefore, Deep Learning systems are increasingly playing an important role in day-to-day operations of our urban systems and hence cannot not be treated as standalone systems anymore. This naturally raises questions from a security viewpoint. Are modern ML systems robust to adversarial attacks for deployment in critical real-world applications? If not, then how can we make progress in securing these systems against such attacks? In this thesis we first demonstrate the vulnerability of modern ML systems on a real world scenario relevant to transportation networks by successfully attacking a commercial ML platform using a traffic-camera image. We review different methods of defense and various challenges associated in training an adversarially robust classifier. In terms of contributions, we propose and investigate a new method of defense to build adversarially robust classifiers using Error-Correcting Codes (ECCs). The idea of using Error-Correcting Codes for multi-class classification has been investigated in the past but only under nominal settings. We build upon this idea in the context of adversarial robustness of Deep Neural Networks. Following the guidelines of code-book design from literature, we formulate a discrete optimization problem to generate codebooks in a systematic manner. This optimization problem maximizes minimum hamming distance between codewords of the codebook while maintaining high column separation. Using the optimal solution of the discrete optimization problem as our codebook, we then build a (robust) multi-class classifier from that codebook. To estimate the adversarial accuracy of ECC based classifiers resulting from different codebooks, we provide methods to generate gradient based white-box attacks. We discuss estimation of class probability estimates (or scores) which are in itself useful for real-world applications along with their use in generating black-box and white-box attacks. We also discuss differentiable decoding methods, which can also be used to generate white-box attacks. We are able to outperform standard all-pairs codebook, providing evidence to the fact that compact codebooks generated using our discrete optimization approach can indeed provide high performance. Most importantly, we show that ECC based classifiers can be partially robust even without any adversarial training. We also show that this robustness is simply not a manifestation of the large network capacity of the overall classifier. Our approach can be seen as the first step towards designing classifiers which are robust by design. These contributions suggest that ECCs based approach can be useful to improve the robustness of modern ML systems and thus making urban systems more resilient to adversarial attacks.
Adversarial Machine Learning
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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
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Author : Yevgeniy Tu
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Adversarial Machine Learning written by Yevgeniy Tu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Computers categories.
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.
Robust Machine Learning
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Author : Rachid Guerraoui
language : en
Publisher: Springer Nature
Release Date :
Robust Machine Learning written by Rachid Guerraoui and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Perturbations Optimization And Statistics
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Author : Tamir Hazan
language : en
Publisher: MIT Press
Release Date : 2023-12-05
Perturbations Optimization And Statistics written by Tamir Hazan and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-05 with Computers categories.
A description of perturbation-based methods developed in machine learning to augment novel optimization methods with strong statistical guarantees. In nearly all machine learning, decisions must be made given current knowledge. Surprisingly, making what is believed to be the best decision is not always the best strategy, even when learning in a supervised learning setting. An emerging body of work on learning under different rules applies perturbations to decision and learning procedures. These methods provide simple and highly efficient learning rules with improved theoretical guarantees. This book describes perturbation-based methods developed in machine learning to augment novel optimization methods with strong statistical guarantees, offering readers a state-of-the-art overview. Chapters address recent modeling ideas that have arisen within the perturbations framework, including Perturb & MAP, herding, and the use of neural networks to map generic noise to distribution over highly structured data. They describe new learning procedures for perturbation models, including an improved EM algorithm and a learning algorithm that aims to match moments of model samples to moments of data. They discuss understanding the relation of perturbation models to their traditional counterparts, with one chapter showing that the perturbations viewpoint can lead to new algorithms in the traditional setting. And they consider perturbation-based regularization in neural networks, offering a more complete understanding of dropout and studying perturbations in the context of deep neural networks.
Strengthening Deep Neural Networks
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Author : Katy Warr
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2019-07-03
Strengthening Deep Neural Networks written by Katy Warr and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-03 with Computers categories.
As deep neural networks (DNNs) become increasingly common in real-world applications, the potential to deliberately "fool" them with data that wouldn’t trick a human presents a new attack vector. This practical book examines real-world scenarios where DNNs—the algorithms intrinsic to much of AI—are used daily to process image, audio, and video data. Author Katy Warr considers attack motivations, the risks posed by this adversarial input, and methods for increasing AI robustness to these attacks. If you’re a data scientist developing DNN algorithms, a security architect interested in how to make AI systems more resilient to attack, or someone fascinated by the differences between artificial and biological perception, this book is for you. Delve into DNNs and discover how they could be tricked by adversarial input Investigate methods used to generate adversarial input capable of fooling DNNs Explore real-world scenarios and model the adversarial threat Evaluate neural network robustness; learn methods to increase resilience of AI systems to adversarial data Examine some ways in which AI might become better at mimicking human perception in years to come
Safe And Trustworthy Machine Learning
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Author : Bhavya Kailkhura
language : en
Publisher: Frontiers Media SA
Release Date : 2021-10-29
Safe And Trustworthy Machine Learning written by Bhavya Kailkhura and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-29 with Science categories.