Adversarial Robustness In 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 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
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.
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 Robustness In Machine Learning
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Author : Muni Sreenivas Pydi
language : en
Publisher:
Release Date : 2022
Adversarial Robustness In Machine Learning written by Muni Sreenivas Pydi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Deep learning based classification algorithms perform poorly on adversarially perturbed data. Adversarial risk quantifies the performance of a classifier in the presence of an adversary. Numerous definitions of adversarial risk---not all mathematically rigorous and differing subtly in the details---have appeared in the literature. Adversarial attacks are designed to increase the adversarial risk of classifiers, and robust classifiers are sought that can resist such attacks. It was hitherto unknown what the theoretical limits on adversarial risk are, and whether there is an equilibrium in the game between the classifier and the adversary. In this thesis, we establish a mathematically rigorous foundation for adversarial robustness, derive algorithm-independent bounds on adversarial risk, and provide alternative characterizations based on distributional robustness and game theory. Key to these results are the numerous connections we discover between adversarial robustness and optimal transport theory. We begin by examining various definitions for adversarial risk, and laying down conditions for their measurability and equivalences. In binary classification with 0-1 loss, we show that the optimal adversarial risk is determined by an optimal transport cost between the probability distributions of the two classes. Using the couplings that achieve this cost, we derive the optimal robust classifiers for several univariate distributions. Using our results, we compute lower bounds on adversarial risk for several real-world datasets. We extend our results to general loss functions under convexity and smoothness assumptions. We close with alternative characterizations for adversarial robustness that lead to the proof of a pure Nash equilibrium in the two-player game between the adversary and the classifier. We show that adversarial risk is identical to the minimax risk in a robust hypothesis testing problem with Wasserstein uncertainty sets. Moreover, the optimal adversarial risk is the Bayes error between a worst-case pair of distributions belonging to these sets. Our theoretical results lead to several algorithmic insights for practitioners and motivate further study on the intersection of adversarial robustness and optimal transport.
Advances In Reliably Evaluating And Improving Adversarial Robustness
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Author : Jonas Rauber
language : en
Publisher:
Release Date : 2021
Advances In Reliably Evaluating And Improving Adversarial Robustness written by Jonas Rauber 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.
Machine learning has made enormous progress in the last five to ten years. We can now make a computer, a machine, learn complex perceptual tasks from data rather than explicitly programming it. When we compare modern speech or image recognition systems to those from a decade ago, the advances are awe-inspiring. The susceptibility of machine learning systems to small, maliciously crafted adversarial perturbations is less impressive. Almost imperceptible pixel shifts or background noises can completely derail their performance. While humans are often amused by the stupidity of artificial intelligence, engineers worry about the security and safety of their machine learning applications, and scientists wonder how to make machine learning models more robust and more human-like. This dissertation summarizes and discusses advances in three areas of adversarial robustness. First, we introduce a new type of adversarial attack against machine learning models in real-world black-box scenarios. Unlike previous attacks, it does not require any insider knowledge or special access. Our results demonstrate the concrete threat caused by the current lack of robustness in machine learning applications. Second, we present several contributions to deal with the diverse challenges around evaluating adversarial robustness. The most fundamental challenge is that common attacks cannot distinguish robust models from models with misleading gradients. We help uncover and solve this problem through two new types of attacks immune to gradient masking. Misaligned incentives are another reason for insufficient evaluations. We published joint guidelines and organized an interactive competition to mitigate this problem. Finally, our open-source adversarial attacks library Foolbox empowers countless researchers to overcome common technical obstacles. Since robustness evaluations are inherently unstandardized, straightforward access to various attacks is more than a technical convenience; it promotes thorough evaluations. Third, we showcase a fundamentally new neural network architecture for robust classification. It uses a generative analysis-by-synthesis approach. We demonstrate its robustness using a digit recognition task and simultaneously reveal the limitations of prior work that uses adversarial training. Moreover, further studies have shown that our model best predicts human judgments on so-called controversial stimuli and that our approach scales to more complex datasets.
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.
Artificial Neural Networks And Machine Learning Icann 2021
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Author : Igor Farkaš
language : en
Publisher: Springer Nature
Release Date : 2021-09-11
Artificial Neural Networks And Machine Learning Icann 2021 written by Igor Farkaš and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-11 with Computers categories.
The proceedings set LNCS 12891, LNCS 12892, LNCS 12893, LNCS 12894 and LNCS 12895 constitute the proceedings of the 30th International Conference on Artificial Neural Networks, ICANN 2021, held in Bratislava, Slovakia, in September 2021.* The total of 265 full papers presented in these proceedings was carefully reviewed and selected from 496 submissions, and organized in 5 volumes. In this volume, the papers focus on topics such as adversarial machine learning, anomaly detection, attention and transformers, audio and multimodal applications, bioinformatics and biosignal analysis, capsule networks and cognitive models. *The conference was held online 2021 due to the COVID-19 pandemic.
Adversarial Robustness In Classification Via The Lens Of Optimal Transport
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Author : Jakwang Kim
language : en
Publisher:
Release Date : 2023
Adversarial Robustness In Classification Via The Lens Of Optimal Transport written by Jakwang Kim and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
Through this thesis, I purse rigorous understanding of adversarial training in multiclass classification problem which is now one of the most important tasks in modern machine learning community. Especially, after the great success of deeplearning based algorithms, there have been a numerous demands to understand the robustness of machine learning models after their training, especially since the discovery that they exhibit a critical vulnerability to adversarial perturbations that are imperceptible to humans was known. Since 2010 there have been numerous papers regarding adversarial training in order to defend against such adversarial attacks, and hence to obtain more robust machine learning models. However, in spite of huge efforts devoted to in this field, until very recently no rigorous mathematical understanding was achieved, to the author's best knowledge. Because of the lack of rigorous understanding regarding this problem, many properties, or even the existence of such robust classifiers, is unveiled. Since this problem has both interesting and important for both theoretical and practical reasons, the ultimate goal of this thesis is not only to provide the mathemat- ically rigorous foundation for the adversarial training in multiclass classification but also to propose practical algorithms. Part of new algorithms we purse in this thesis is guided by the new mathematical framework for adversarial training perspective that we develop. The contributions of this thesis can be summarized in the following three themes, which we will develop in succeeding three chapter: 1. In chapter 3, Adversarial learning, Generalized barycenter problem and their connection by multimarginal optimal transport, the adversarial training problem is connected to multimarginal optimal transport problem. To obtain this beauti- ful connection, the generalized (Wasserstein) barycenter problem which is indeed the generalization of classical barycenter problem is introduced. Based on that, various equivalent formulas are derived including a multimarginal optimal transport formulation. Through these equivalent formulations we are able to prove that the adversarial training problem, especially, distributional-perturbing adversarial training model, is equivalent to the generalized barycenter problem and the associating multimarginal optimal transport problem. One of advantages of these equivalences is that it allows to use many computational optimal transport tool to calculate the adversarial risk. We will leverage such computational advantages in chapter 5. 2. In chapter 4, On the existence of solutions to adversarial training in multiclass classification, the mathematical understanding of three variant adversarial training models is pursued. In particular, we show that the well-posedness of adversarial training models. The existence of Borel measurable optimal robust classifiers is proved in the distributional-perturbing adversarial training model. Furthermore, other two models also impose Borel measurable optimal robust classifiers through the previous result. Lastly, a unifying perspective of all three models is provided, through which we will see that the three models are almost the same. 3. In chapter 5, Two approaches for computing adversarial training problem based on optimal transport frameworks, based on the theoretical understanding of the previous two chapters, we propose two numerical implementations for adversarial training. One employs the geometric structure of the generalized barycenter problem in chapter 3 which suggests a way to count all possible interactions efficiently. The other relies on a multimarginal optimal transport formulation of the adversarial training problem, also developed in chapter 3, which implicitly hints the idea of truncation. Numerical results on real data sets obtained by these algorithms are also provided.
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.
Machine Learning And Knowledge Discovery In Databases Research Track
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Author : Danai Koutra
language : en
Publisher: Springer Nature
Release Date : 2023-09-16
Machine Learning And Knowledge Discovery In Databases Research Track written by Danai Koutra and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-16 with Computers categories.
The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023. The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track. The volumes are organized in topical sections as follows: Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering. Part II: Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning. Part III: Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning. Part IV: Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning. Part V: Robustness; Time Series; Transfer and Multitask Learning. Part VI: Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval. Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.