Robust Machine Learning in Adversarial Setting with Provable Guarantee

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

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

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 attack methods is empirically used as a proxy to adversarial robustness. However, failure of attack methods to find adversarial perturbations cannot be equated with being robust. In this work, we

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.

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

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

This book captures the state of the art research in the area of malicious code detection, prevention and mitigation. It contains cutting-edge behavior-based techniques to analyze and detect obfuscated malware. The book analyzes current trends in malware activity online, including botnets and malicious code for profit, and it proposes effective models for detection and prevention of attacks using. Furthermore, the book introduces novel techniques for creating services that protect their own integrity and safety, plus the data they manage.

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.

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.

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

The majority of natural language processing (NLP) is English language processing, and while there is good language technology support for (standard varieties of) English, support for Albanian, Burmese, or Cebuano—and most other languages—remains limited. Being able to bridge this digital divide is important for scientific and democratic reasons but also represents an enormous growth potential. A key challenge for this to happen is learning to align basic meaning-bearing units of different languages. In this book, the authors survey

The Conference brings together academia and industry professionals as well as students and researchers to present and discuss state of the art solutions and research results on existing and future signal processing and telecommunication systems, applications, and related standardization activities The conference will feature oral and poster sessions and special sessions in all areas of signal processing and communications theory, systems and applications

Support vector machines (SVM) have both a solid mathematical background and practical applications. This book focuses on the recent advances and applications of the SVM, such as image processing, medical practice, computer vision, and pattern recognition, machine learning, applied statistics, and artificial intelligence. The aim of this book is to create a comprehensive source on support vector machine applications.