Robust Machine Learning in Adversarial Setting with Provable Guarantee

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  • Author : Yizhen Wang
  • Publisher : Unknown
  • Pages : 178 pages
  • ISBN :
  • Rating : /5 from reviews
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Download or Read online Robust Machine Learning in Adversarial Setting with Provable Guarantee full in PDF, ePub and kindle. this book written by Yizhen Wang and published by Unknown which was released on 18 May 2021 with total page 178 pages. We cannot guarantee that Robust Machine Learning in Adversarial Setting with Provable Guarantee book is available in the library, click Get Book button and read full online book in your kindle, tablet, IPAD, PC or mobile whenever and wherever You Like. 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 : 18 May 2021
GET THIS BOOK Robust Machine Learning in Adversarial Setting with Provable Guarantee

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

Adversarial Machine Learning

Adversarial Machine Learning
  • Author : Yevgeniy Vorobeychik,Murat Kantarcioglu
  • Publisher : Morgan & Claypool Publishers
  • Release : 08 August 2018
GET THIS BOOK Adversarial Machine Learning

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

Machine Learning and Knowledge Discovery in Databases

Machine Learning and Knowledge Discovery in Databases
  • Author : Peggy Cellier,Kurt Driessens
  • Publisher : Springer Nature
  • Release : 27 March 2020
GET THIS BOOK Machine Learning and Knowledge Discovery in Databases

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 from 200 submissions. The two volumes (CCIS 1167 and CCIS 1168) present the papers that have been accepted for the following workshops: Workshop on Automating Data Science, ADS 2019; Workshop on Advances in Interpretable

Science of Cyber Security

Science of Cyber Security
  • Author : Feng Liu,Jia Xu,Shouhuai Xu,Moti Yung
  • Publisher : Springer Nature
  • Release : 11 January 2020
GET THIS BOOK Science of Cyber Security

This book constitutes the proceedings of the Second International Conference on Science of Cyber Security, SciSec 2019, held in Nanjing, China, in August 2019. The 20 full papers and 8 short papers presented in this volume were carefully reviewed and selected from 62 submissions. These papers cover the following subjects: Artificial Intelligence for Cybersecurity, Machine Learning for Cybersecurity, and Mechanisms for Solving Actual Cybersecurity Problems (e.g., Blockchain, Attack and Defense; Encryptions with Cybersecurity Applications).

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 : 07 November 2020
GET THIS BOOK Engineering Dependable and Secure Machine Learning Systems

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 and quality assurance of ML software systems, adversarial attacks on ML software systems, adversarial ML and software engineering, etc.

Towards Robust Deep Neural Networks

Towards Robust Deep Neural Networks
  • Author : Andras Rozsa
  • Publisher : Unknown
  • Release : 18 May 2021
GET THIS BOOK Towards Robust Deep Neural Networks

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 powerful machine learning models have managed to reach or even surpass human level performance on various challenging tasks, including object or face recognition in photographs. Although we are capable of

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 : 22 August 2019
GET THIS BOOK Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies

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.

Strengthening Deep Neural Networks

Strengthening Deep Neural Networks
  • Author : Katy Warr
  • Publisher : O'Reilly Media
  • Release : 03 July 2019
GET THIS BOOK Strengthening Deep Neural Networks

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

Machine Learning in Adversarial Settings

Machine Learning in Adversarial Settings
  • Author : Hossein Hosseini
  • Publisher : Unknown
  • Release : 18 May 2021
GET THIS BOOK Machine Learning in Adversarial Settings

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 banking, medical diagnosis, and autonomous cars, it is important to study and evaluate their performance in adversarial settings. The security of machine learning systems has been studied from different perspectives.

Interpretable Machine Learning with Python

Interpretable Machine Learning with Python
  • Author : Serg Masís
  • Publisher : Packt Publishing Ltd
  • Release : 26 March 2021
GET THIS BOOK Interpretable Machine Learning with Python

This hands-on book will help you make your machine learning models fairer, safer, and more reliable and in turn improve business outcomes. Every chapter introduces a new mission where you learn how to apply interpretation methods to realistic use cases with methods that work for any model type as well as methods specific for deep neural networks.

Reliable Machine Learning Via Distributional Robustness

Reliable Machine Learning Via Distributional Robustness
  • Author : Hongseok Namkoong
  • Publisher : Unknown
  • Release : 18 May 2021
GET THIS BOOK Reliable Machine Learning Via Distributional Robustness

As machine learning systems increasingly get applied in high-stake domains such as autonomous vehicles and medical diagnosis, it is imperative that they maintain good performance when deployed. Modeling assumptions rarely hold due to noisy inputs, shifts in environment, unmeasured confounders, and even adversarial attacks to the system. The standard machine learning paradigm that optimize average performance is brittle to even small amounts of noise, and exhibit poor performance on underrepresented minority groups. We study \emph{distributionally robust} learning procedures that

Artificial Neural Networks and Machine Learning ICANN 2019 Image Processing

Artificial Neural Networks and Machine Learning     ICANN 2019  Image Processing
  • Author : Igor V. Tetko,Věra Kůrková,Pavel Karpov,Fabian Theis
  • Publisher : Springer Nature
  • Release : 03 November 2019
GET THIS BOOK Artificial Neural Networks and Machine Learning ICANN 2019 Image Processing

The proceedings set LNCS 11727, 11728, 11729, 11730, and 11731 constitute the proceedings of the 28th International Conference on Artificial Neural Networks, ICANN 2019, held in Munich, Germany, in September 2019. The total of 277 full papers and 43 short papers presented in these proceedings was carefully reviewed and selected from 494 submissions. They were organized in 5 volumes focusing on theoretical neural computation; deep learning; image processing; text and time series; and workshop and special sessions.