Evaluating And Understanding Adversarial Robustness In Deep Learning

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Evaluating And Understanding Adversarial Robustness In Deep Learning

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Author : Jinghui Chen
language : en
Publisher:
Release Date : 2021

Evaluating And Understanding Adversarial Robustness In Deep Learning written by Jinghui Chen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.

Deep Neural Networks (DNNs) have made many breakthroughs in different areas of artificial intelligence. However, recent studies show that DNNs are vulnerable to adversarial examples. A tiny perturbation on an image that is almost invisible to human eyes could mislead a well-trained image classifier towards misclassification. This raises serious security concerns and trustworthy issues towards the robustness of Deep Neural Networks in solving real world challenges. Researchers have been working on this problem for a while and it has further led to a vigorous arms race between heuristic defenses that propose ways to defend against existing attacks and newly-devised attacks that are able to penetrate such defenses. While the arm race continues, it becomes more and more crucial to accurately evaluate model robustness effectively and efficiently under different threat models and identify those ``falsely'' robust models that may give us a false sense of robustness. On the other hand, despite the fast development of various kinds of heuristic defenses, their practical robustness is still far from satisfactory, and there are actually little algorithmic improvements in terms of defenses during recent years. This suggests that there still lacks further understandings toward the fundamentals of adversarial robustness in deep learning, which might prevent us from designing more powerful defenses. \\The overarching goal of this research is to enable accurate evaluations of model robustness under different practical settings as well as to establish a deeper understanding towards other factors in the machine learning training pipeline that might affect model robustness. Specifically, we develop efficient and effective Frank-Wolfe attack algorithms under white-box and black-box settings and a hard-label adversarial attack, RayS, which is capable of detecting ``falsely'' robust models. In terms of understanding adversarial robustness, we propose to theoretically study the relationship between model robustness and data distributions, the relationship between model robustness and model architectures, as well as the relationship between model robustness and loss smoothness. The techniques proposed in this dissertation form a line of researches that deepens our understandings towards adversarial robustness and could further guide us in designing better and faster robust training methods.

Adversarial Machine Learning

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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.

Strengthening Deep Neural Networks

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Author : Katy Warr
language : en
Publisher: O'Reilly Media
Release Date : 2019-07-03

Strengthening Deep Neural Networks written by Katy Warr and has been published by O'Reilly Media 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

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 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

Proceedings Of The 6th International Conference On Deep Learning Artificial Intelligence And Robotics Icdlair 2024

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Author : Priyanka Ahlawat
language : en
Publisher: Springer Nature
Release Date : 2025-07-26

Proceedings Of The 6th International Conference On Deep Learning Artificial Intelligence And Robotics Icdlair 2024 written by Priyanka Ahlawat and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-26 with Computers categories.

This is an open access book. The proposed conference ICDLAIR 2024 represents key ingredients for the 5G. The extensive application of AI and DL is dramatically changing products and services, with a large impact on labour, economy and society at all. ICDLAIR 2024, organized by NIT Kurukshetra, India in collaboration with International Association of Academicians (IAASSE), Emlyon Business School France and CSUSB USA, aims at collecting scientific and technical contributions with respect to models, tools, technologies and applications in the field of modern artificial intelligence and robotics, covering the entire range of concepts from theory to practice, including case studies, works-in-progress, and conceptual explorations. Through sharing and networking, ICDLAIR 2024 will provide an opportunity for researchers, practitioners and educators to exchange research evidence, practical experiences and innovative ideas on issues related to the Conference theme. ICDLAIR 2024 intends to publish the post-conference work in order to give authors the opportunity to collect feedback during the presentation.

Computer Vision Eccv 2022

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Author : Shai Avidan
language : en
Publisher: Springer Nature
Release Date : 2022-11-05

Computer Vision Eccv 2022 written by Shai Avidan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-05 with Computers categories.

The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.

Neural Information Processing

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Author : Mohammad Tanveer
language : en
Publisher: Springer Nature
Release Date : 2023-04-14

Neural Information Processing written by Mohammad Tanveer and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-14 with Computers categories.

The four-volume set CCIS 1791, 1792, 1793 and 1794 constitutes the refereed proceedings of the 29th International Conference on Neural Information Processing, ICONIP 2022, held as a virtual event, November 22–26, 2022. The 213 papers presented in the proceedings set were carefully reviewed and selected from 810 submissions. They were organized in topical sections as follows: Theory and Algorithms; Cognitive Neurosciences; Human Centered Computing; and Applications. The ICONIP conference aims to provide a leading international forum for researchers, scientists, and industry professionals who are working in neuroscience, neural networks, deep learning, and related fields to share their new ideas, progress, and achievements.

Attacks Defenses And Testing For Deep Learning

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Author : Jinyin Chen
language : en
Publisher: Springer Nature
Release Date : 2024-06-03

Attacks Defenses And Testing For Deep Learning written by Jinyin Chen and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-03 with Computers categories.

This book provides a systematic study on the security of deep learning. With its powerful learning ability, deep learning is widely used in CV, FL, GNN, RL, and other scenarios. However, during the process of application, researchers have revealed that deep learning is vulnerable to malicious attacks, which will lead to unpredictable consequences. Take autonomous driving as an example, there were more than 12 serious autonomous driving accidents in the world in 2018, including Uber, Tesla and other high technological enterprises. Drawing on the reviewed literature, we need to discover vulnerabilities in deep learning through attacks, reinforce its defense, and test model performance to ensure its robustness. Attacks can be divided into adversarial attacks and poisoning attacks. Adversarial attacks occur during the model testing phase, where the attacker obtains adversarial examples by adding small perturbations. Poisoning attacks occur during the model training phase, wherethe attacker injects poisoned examples into the training dataset, embedding a backdoor trigger in the trained deep learning model. An effective defense method is an important guarantee for the application of deep learning. The existing defense methods are divided into three types, including the data modification defense method, model modification defense method, and network add-on method. The data modification defense method performs adversarial defense by fine-tuning the input data. The model modification defense method adjusts the model framework to achieve the effect of defending against attacks. The network add-on method prevents the adversarial examples by training the adversarial example detector. Testing deep neural networks is an effective method to measure the security and robustness of deep learning models. Through test evaluation, security vulnerabilities and weaknesses in deep neural networks can be identified. By identifying and fixing these vulnerabilities, the security and robustness of the model can be improved. Our audience includes researchers in the field of deep learning security, as well as software development engineers specializing in deep learning.

Low Rank Models In Visual Analysis

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Author : Zhouchen Lin
language : en
Publisher: Academic Press
Release Date : 2017-06-06

Low Rank Models In Visual Analysis written by Zhouchen Lin and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-06-06 with Computers categories.

Low-Rank Models in Visual Analysis: Theories, Algorithms, and Applications presents the state-of-the-art on low-rank models and their application to visual analysis. It provides insight into the ideas behind the models and their algorithms, giving details of their formulation and deduction. The main applications included are video denoising, background modeling, image alignment and rectification, motion segmentation, image segmentation and image saliency detection. Readers will learn which Low-rank models are highly useful in practice (both linear and nonlinear models), how to solve low-rank models efficiently, and how to apply low-rank models to real problems. - Presents a self-contained, up-to-date introduction that covers underlying theory, algorithms and the state-of-the-art in current applications - Provides a full and clear explanation of the theory behind the models - Includes detailed proofs in the appendices

Interpretable Machine Learning

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Author : Christoph Molnar
language : en
Publisher: Lulu.com
Release Date : 2020

Interpretable Machine Learning written by Christoph Molnar and has been published by Lulu.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Computers categories.

This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.