Robust Machine Learning in Adversarial Setting with Provable Guarantee

Written By Yizhen Wang
Robust Machine Learning in Adversarial Setting with Provable Guarantee
  • Publsiher : Unknown
  • Release : 11 April 2021
  • ISBN :
  • Pages : 178 pages
  • Rating : /5 from reviews
GET THIS BOOKRobust Machine Learning in Adversarial Setting with Provable Guarantee


Download or read book entitled Robust Machine Learning in Adversarial Setting with Provable Guarantee by author: Yizhen Wang which was release on 11 April 2021 and published by Unknown with total page 178 pages . This book available in PDF, EPUB and Kindle Format. Over the last decade, machine learning systems have achieved state-of-the-art performance in many fields, and are now used in increasing number of applications. However, recent research work has revealed multiple attacks to machine learning systems that significantly reduce the performance by manipulating the training or test data. As machine learning is increasingly involved in high-stake decision making processes, the robustness of machine learning systems in adversarial environment becomes a major concern. This dissertation attempts to build machine learning systems robust to such adversarial manipulation with the emphasis on providing theoretical performance guarantees. We consider adversaries in both test and training time, and make the following contributions. First, we study the robustness of machine learning algorithms and model to test-time adversarial examples. We analyze the distributional and finite sample robustness of nearest neighbor classification, and propose a modified 1-Nearest-Neighbor classifier that both has theoretical guarantee and empirical improvement in robustness. Second, we examine the robustness of malware detectors to program transformation. We propose novel attacks that evade existing detectors using program transformation, and then show program normalization as a provably robust defense against such transformation. Finally, we investigate data poisoning attacks and defenses for online learning, in which models update and predict over data stream in real-time. We show efficient attacks for general adversarial objectives, analyze the conditions for which filtering based defenses are effective, and provide practical guidance on choosing defense mechanisms and parameters.

Robust Machine Learning in Adversarial Setting with Provable Guarantee

Robust Machine Learning in Adversarial Setting with Provable Guarantee
  • Author : Yizhen Wang
  • Publisher : Unknown
  • Release Date : 2020
  • Total pages : 178
  • ISBN :
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Summary : Over the last decade, machine learning systems have achieved state-of-the-art performance in many fields, and are now used in increasing number of applications. However, recent research work has revealed multiple attacks to machine learning systems that significantly reduce the performance by manipulating the training or test data. As machine learning ...

Adversarial Machine Learning

Adversarial Machine Learning
  • Author : Yevgeniy Vorobeychik,Murat Kantarcioglu
  • Publisher : Morgan & Claypool Publishers
  • Release Date : 2018-08-08
  • Total pages : 169
  • ISBN :
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Summary : 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 ...

Improved Methodology for Evaluating Adversarial Robustness in Deep Neural Networks

Improved Methodology for Evaluating Adversarial Robustness in Deep Neural Networks
  • Author : Kyungmi Lee (S. M.)
  • Publisher : Unknown
  • Release Date : 2020
  • Total pages : 93
  • ISBN :
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Summary : Deep neural networks are known to be vulnerable to adversarial perturbations, which are often imperceptible to humans but can alter predictions of machine learning systems. Since the exact value of adversarial robustness is difficult to obtain for complex deep neural networks, accuracy of the models against perturbed examples generated by ...

Strengthening Deep Neural Networks

Strengthening Deep Neural Networks
  • Author : Katy Warr
  • Publisher : O'Reilly Media
  • Release Date : 2019-07-03
  • Total pages : 246
  • ISBN :
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Summary : 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 ...

Intelligent Systems and Applications

Intelligent Systems and Applications
  • Author : Kohei Arai
  • Publisher : Springer Nature
  • Release Date : 2021-04-11
  • Total pages : 212
  • ISBN :
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Summary : Read online Intelligent Systems and Applications written by Kohei Arai, published by Springer Nature which was released on . Download full Intelligent Systems and Applications Books now! Available in PDF, ePub and Kindle....

Machine Learning and Knowledge Discovery in Databases

Machine Learning and Knowledge Discovery in Databases
  • Author : Peggy Cellier,Kurt Driessens
  • Publisher : Springer Nature
  • Release Date : 2020-03-27
  • Total pages : 679
  • ISBN :
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Summary : This two-volume set constitutes the refereed proceedings of the workshops which complemented the 19th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in Würzburg, Germany, in September 2019. The 70 full papers and 46 short papers presented in the two-volume set were carefully reviewed and selected ...

Engineering Dependable and Secure Machine Learning Systems

Engineering Dependable and Secure Machine Learning Systems
  • Author : Onn Shehory,Eitan Farchi,Guy Barash
  • Publisher : Springer Nature
  • Release Date : 2020-11-07
  • Total pages : 141
  • ISBN :
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Summary : This book constitutes the revised selected papers of the Third International Workshop on Engineering Dependable and Secure Machine Learning Systems, EDSMLS 2020, held in New York City, NY, USA, in February 2020. The 7 full papers and 3 short papers were thoroughly reviewed and selected from 16 submissions. The volume presents original research on dependability ...

Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies

Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies
  • Author : National Academies of Sciences, Engineering, and Medicine,Division on Engineering and Physical Sciences,Computer Science and Telecommunications Board,Board on Mathematical Sciences and Analytics,Intelligence Community Studies Board
  • Publisher : National Academies Press
  • Release Date : 2019-08-22
  • Total pages : 82
  • ISBN :
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Summary : 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 ...

Towards Robust Deep Neural Networks

Towards Robust Deep Neural Networks
  • Author : Andras Rozsa
  • Publisher : Unknown
  • Release Date : 2018
  • Total pages : 150
  • ISBN :
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Summary : One of the greatest technological advancements of the 21st century has been the rise of machine learning. This thriving field of research already has a great impact on our lives and, considering research topics and the latest advancements, will continue to rapidly grow. In the last few years, the most ...

Artificial Neural Networks and Machine Learning ICANN 2020

Artificial Neural Networks and Machine Learning     ICANN 2020
  • Author : Igor Farkaš
  • Publisher : Springer Nature
  • Release Date : 2021-04-11
  • Total pages : 212
  • ISBN :
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Summary : Read online Artificial Neural Networks and Machine Learning ICANN 2020 written by Igor Farkaš, published by Springer Nature which was released on . Download full Artificial Neural Networks and Machine Learning ICANN 2020 Books now! Available in PDF, ePub and Kindle....

Towards Adversarial Robustness of Feed forward and Recurrent Neural Networks

Towards Adversarial Robustness of Feed forward and Recurrent Neural Networks
  • Author : Qinglong Wang
  • Publisher : Unknown
  • Release Date : 2020
  • Total pages : 212
  • ISBN :
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Summary : "Recent years witnessed the successful resurgence of neural networks through the lens of deep learning research. As the spread of deep neural network (DNN) continues to reach multifarious branches of research, including computer vision, natural language processing, and malware detection, it has been found that the vulnerability of these powerful ...

Machine Learning and Knowledge Discovery in Databases

Machine Learning and Knowledge Discovery in Databases
  • Author : Frank Hutter
  • Publisher : Springer Nature
  • Release Date : 2021-04-11
  • Total pages : 212
  • ISBN :
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Summary : Read online Machine Learning and Knowledge Discovery in Databases written by Frank Hutter, published by Springer Nature which was released on . Download full Machine Learning and Knowledge Discovery in Databases Books now! Available in PDF, ePub and Kindle....

Secure and Private Machine Learning for Smart Devices

Secure and Private Machine Learning for Smart Devices
  • Author : MOUSTAFA FARID TAHA MOHAMMED ALZANTOT
  • Publisher : Unknown
  • Release Date : 2019
  • Total pages : 185
  • ISBN :
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Summary : Nowadays, machine learning models and especially deep neural networks are achieving outstanding levels of accuracy in different tasks such as image understanding and speech recognition. Therefore, they are widely used in the pervasive smart connected devices, e.g., smartphones, security cameras, and digital personal assistants, to make intelligent inferences from ...

Machine Learning in Adversarial Settings

Machine Learning in Adversarial Settings
  • Author : Hossein Hosseini
  • Publisher : Unknown
  • Release Date : 2019
  • Total pages : 111
  • ISBN :
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Summary : Deep neural networks have achieved remarkable success over the last decade in a variety of tasks. Such models are, however, typically designed and developed with the implicit assumption that they will be deployed in benign settings. With the increasing use of learning systems in security-sensitive and safety-critical application, such as ...

Characterizing the Limits and Defenses of Machine Learning in Adversarial Settings

Characterizing the Limits and Defenses of Machine Learning in Adversarial Settings
  • Author : Nicolas Papernot
  • Publisher : Unknown
  • Release Date : 2018
  • Total pages : 212
  • ISBN :
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Summary : Advances in machine learning (ML) in recent years have enabled a dizzying array of applications such as object recognition, autonomous systems, security diagnostics, and playing the game of Go. Machine learning is not only a new paradigm for building software and systems, it is bringing social disruption at scale. There ...