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On The Evaluation Of Adversarial Vulnerabilities Of Deep Neural Networks
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Book Synopsis On the Evaluation of Adversarial Vulnerabilities of Deep Neural Networks by : Mehrgan Khoshpasand Foumani
Download or read book On the Evaluation of Adversarial Vulnerabilities of Deep Neural Networks written by Mehrgan Khoshpasand Foumani and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Adversarial examples are inputs designed by an adversary to fool the machine learning models. Despite having overly simplifying assumptions and extensive studies in recent years, there is no defense against adversarial examples for complex tasks (e.g., ImageNet). However, for more straightforward tasks like hand-written digit classification, a robust model seems to be within reach. Having the right estimation of the adversarial robustness of the models has been one of the main challenges researchers face. First, we present AETorch, a Pytorch library containing the strongest attacks and the common threat models that make comparing the defenses easier. Most of the research about adversarial examples have focused on adding small l[subscript p] bounded perturbations to the natural inputs with the assumption that the actual label remains unchanged. However, being robust in l[subscript p] bounded settings does not guarantee general robustness. Additionally, we present an efficient technique to create unrestricted adversarial examples using generative adversarial networks on MNIST, SVHN, and fashion-mnist datasets. We demonstrate that even the state-of-the-art adversarially robust MNIST classifiers are vulnerable to the adversarial examples generated with this technique. We demonstrate that our method is better than the previous unrestricted techniques since it has access to a bigger adversarial subspace. Additionally, we show that examples generated with our method are transferable. Overall, our findings emphasize the need for further studying the vulnerability of neural networks to unrestricted adversarial examples.
Book Synopsis Attacks, Defenses and Testing for Deep Learning by : Jinyin Chen
Download or read book Attacks, Defenses and Testing for Deep Learning written by Jinyin Chen and published by Springer Nature. This book was released on with total page 413 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Strengthening Deep Neural Networks by : Katy Warr
Download or read book Strengthening Deep Neural Networks written by Katy Warr and published by "O'Reilly Media, Inc.". This book was released on 2019-07-03 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Adversarial Machine Learning by : Aneesh Sreevallabh Chivukula
Download or read book Adversarial Machine Learning written by Aneesh Sreevallabh Chivukula and published by Springer Nature. This book was released on 2023-03-06 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis The Good, the Bad and the Ugly by : Xiaoting Li
Download or read book The Good, the Bad and the Ugly written by Xiaoting Li and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks have been widely adopted to address different real-world problems. Despite the remarkable achievements in machine learning tasks, they remain vulnerable to adversarial examples that are imperceptible to humans but can mislead the state-of-the-art models. More specifically, such adversarial examples can be generalized to a variety of common data structures, including images, texts and networked data. Faced with the significant threat that adversarial attacks pose to security-critical applications, in this thesis, we explore the good, the bad and the ugly of adversarial machine learning. In particular, we focus on the investigation on the applicability of adversarial attacks in real-world scenarios for social good and their defensive paradigms. The rapid progress of adversarial attacking techniques aids us to better understand the underlying vulnerabilities of neural networks that inspires us to explore their potential usage for good purposes. In real world, social media has extremely reshaped our daily life due to their worldwide accessibility, but its data privacy also suffers from inference attacks. Based on the fact that deep neural networks are vulnerable to adversarial examples, we attempt a novel perspective of protecting data privacy in social media and design a defense framework called Adv4SG, where we introduce adversarial attacks to forge latent feature representations and mislead attribute inference attacks. Considering that text data in social media shares the most significant privacy of users, we investigate how text-space adversarial attacks can be leveraged to protect users' attributes. Specifically, we integrate social media property to advance Adv4SG, and introduce cost-effective mechanisms to expedite attribute protection over text data under the black-box setting. By conducting extensive experiments on real-world social media datasets, we show that Adv4SG is an appealing method to mitigate the inference attacks. Second, we extend our study to more complex networked data. Social network is more of a heterogeneous environment which is naturally represented as graph-structured data, maintaining rich user activities and complicated relationships among them. This enables attackers to deploy graph neural networks (GNNs) to automate attribute inferences from user features and relationships, which makes such privacy disclosure hard to avoid. To address that, we take advantage of the vulnerability of GNNs to adversarial attacks, and propose a new graph poisoning attack, called AttrOBF to mislead GNNs into misclassification and thus protect personal attribute privacy against GNN-based inference attacks on social networks. AttrOBF provides a more practical formulation through obfuscating optimal training user attribute values for real-world social graphs. Our results demonstrate the promising potential of applying adversarial attacks to attribute protection on social graphs. Third, we introduce a watermarking-based defense strategy against adversarial attacks on deep neural networks. With the ever-increasing arms race between defenses and attacks, most existing defense methods ignore fact that attackers can possibly detect and reproduce the differentiable model, which leaves the window for evolving attacks to adaptively evade the defense. Based on this observation, we propose a defense mechanism that creates a knowledge gap between attackers and defenders by imposing a secret watermarking process into standard deep neural networks. We analyze the experimental results of a wide range of watermarking algorithms in our defense method against state-of-the-art attacks on baseline image datasets, and validate the effectiveness our method in protesting adversarial examples. Our research expands the investigation of enhancing the deep learning model robustness against adversarial attacks and unveil the insights of applying adversary for social good. We design Adv4SG and AttrOBF to take advantage of the superiority of adversarial attacking techniques to protect the social media user's privacy on the basis of discrete textual data and networked data, respectively. Both of them can be realized under the practical black-box setting. We also provide the first attempt at utilizing digital watermark to increase model's randomness that suppresses attacker's capability. Through our evaluation, we validate their effectiveness and demonstrate their promising value in real-world use.
Book Synopsis Machine Learning Safety by : Xiaowei Huang
Download or read book Machine Learning Safety written by Xiaowei Huang and published by Springer Nature. This book was released on 2023-04-28 with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning algorithms allow computers to learn without being explicitly programmed. Their application is now spreading to highly sophisticated tasks across multiple domains, such as medical diagnostics or fully autonomous vehicles. While this development holds great potential, it also raises new safety concerns, as machine learning has many specificities that make its behaviour prediction and assessment very different from that for explicitly programmed software systems. This book addresses the main safety concerns with regard to machine learning, including its susceptibility to environmental noise and adversarial attacks. Such vulnerabilities have become a major roadblock to the deployment of machine learning in safety-critical applications. The book presents up-to-date techniques for adversarial attacks, which are used to assess the vulnerabilities of machine learning models; formal verification, which is used to determine if a trained machine learning model is free of vulnerabilities; and adversarial training, which is used to enhance the training process and reduce vulnerabilities. The book aims to improve readers’ awareness of the potential safety issues regarding machine learning models. In addition, it includes up-to-date techniques for dealing with these issues, equipping readers with not only technical knowledge but also hands-on practical skills.
Book Synopsis Adversarial Machine Learning by : Yevgeniy Vorobeychik
Download or read book Adversarial Machine Learning written by Yevgeniy Vorobeychik and published by Morgan & Claypool Publishers. This book was released on 2018-08-08 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Evaluating and Certifying the Adversarial Robustness of Neural Language Models by : Muchao Ye
Download or read book Evaluating and Certifying the Adversarial Robustness of Neural Language Models written by Muchao Ye and published by . This book was released on 2024 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Language models (LMs) built by deep neural networks (DNNs) have achieved great success in various areas of artificial intelligence, which have played an increasingly vital role in profound applications including chatbots and smart healthcare. Nonetheless, the vulnerability of DNNs against adversarial examples still threatens the application of neural LMs to safety-critical tasks. To specify, DNNs will change their correct predictions into incorrect ones when small perturbations are added to the original input texts. In this dissertation, we identify key challenges in evaluating and certifying the adversarial robustness of neural LMs and bridge those gaps through efficient hard-label text adversarial attacks and a unified certified robust training framework. The first step of developing neural LMs with high adversarial robustness is evaluating whether they are empirically robust against perturbed texts. The vital technique related to that is the text adversarial attack, which aims to construct a text that can fool LMs. Ideally, it shall output high-quality adversarial examples in a realistic setting with high efficiency. However, current evaluation pipelines proposed in the realistic hard-label setting adopt heuristic search methods, consequently meeting an inefficiency problem. To tackle this limitation, we introduce a series of hard-label text adversarial attack methods, which successfully tackle the inefficiency problem by using a pretrained word embedding space as an intermediate. A deep dive into this idea illustrates that utilizing an estimated decision boundary in the introduced word embedding space helps improve the quality of crafted adversarial examples. The ultimate goal of constructing robust neural LMs is obtaining ones for which adversarial examples do not exist, which can be realized through certified robust training. The research community has proposed different types of certified robust training either in the discrete input space or in the continuous latent feature space. We discover the structural gap within current pipelines and unify them in the word embedding space. By removing unnecessary bound computation modules, i.e., interval bound propagation, and harnessing a new decoupled regularization learning paradigm, our unification can provide a stronger robustness guarantee. Given the aforementioned contributions, we believe our findings will help contribute to the development of robust neural LMs.
Book Synopsis Towards Adversarial Robustness of Feed-forward and Recurrent Neural Networks by : Qinglong Wang
Download or read book Towards Adversarial Robustness of Feed-forward and Recurrent Neural Networks written by Qinglong Wang and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "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 models is equally impressive as their capability in classification tasks. Specifically, research on the adversarial example problem exposes that DNNs, albeit powerful when confronted with legitimate samples, suffer severely from adversarial examples. These synthetic examples can be created by slightly modifying legitimate samples. We speculate that this vulnerability may significantly impede an extensive adoption of DNNs in safety-critical domains. This thesis aims to comprehend some of the mysteries of this vulnerability of DNN, design generic frameworks and deployable algorithms to protect DNNs with different architectures from attacks armed with adversarial examples. We first conduct a thorough exploration of existing research on explaining the pervasiveness of adversarial examples. We unify the hypotheses raised in existing work by extracting three major influencing factors, i.e., data, model, and training. These factors are also helpful in locating different attack and defense methods proposed in the research spectrum and analyzing their effectiveness and limitations. Then we perform two threads of research on neural networks with feed-forward and recurrent architectures, respectively. In the first thread, we focus on the adversarial robustness of feed-forward neural networks, which have been widely applied to process images. Under our proposed generic framework, we design two types of adversary resistant feed-forward networks that weaken the destructive power of adversarial examples and even prevent their creation. We theoretically validate the effectiveness of our methods and empirically demonstrate that they significantly boost a DNN's adversarial robustness while maintaining high accuracy in classification. Our second thread of study focuses on the adversarial robustness of the recurrent neural network (RNN), which represents a variety of networks typically used for processing sequential data. We develop an evaluation framework and propose to quantitatively evaluate RNN's adversarial robustness with deterministic finite automata (DFA), which represent rigorous rules and can be extracted from RNNs, and a distance metric suitable for strings. We demonstrate the feasibility of using extracted DFA as rules through conducting careful experimental studies to identify key conditions that affect the extraction performance. Moreover, we theoretically establish the correspondence between different RNNs and different DFA, and empirically validate the correspondence by evaluating and comparing different RNNs for their extraction performance. At last, we develop an algorithm under our framework and conduct a case study to evaluate the adversarial robustness of different RNNs on a set of regular grammars"--
Book Synopsis Evaluation and Design of Robust Neural Network Defenses by : Nicholas Carlini
Download or read book Evaluation and Design of Robust Neural Network Defenses written by Nicholas Carlini and published by . This book was released on 2018 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks provide state-of-the-art results for most machine learning tasks. Unfortunately, neural networks are vulnerable to test-time evasion attacks adversarial examples): inputs specifically designed by an adversary to cause a neural network to misclassify them. This makes applying neural networks in security-critical areas concerning. In this dissertation, we introduce a general framework for evaluating the robustness of neural network through optimization-based methods. We apply our framework to two different domains, image recognition and automatic speech recognition, and find it provides state-of-the-art results for both. To further demonstrate the power of our methods, we apply our attacks to break 14 defenses that have been proposed to alleviate adversarial examples. We then turn to the problem of designing a secure classifier. Given this apparently-fundamental vulnerability of neural networks to adversarial examples, instead of taking an existing classifier and attempting to make it robust, we construct a new classifier which is provably robust by design under a restricted threat model. We consider the domain of malware classification, and construct a neural network classifier that is can not be fooled by an insertion adversary, who can only insert new functionality, and not change existing functionality. We hope this dissertation will provide a useful starting point for both evaluating and constructing neural networks robust in the presence of an adversary.
Book Synopsis Network Security Empowered by Artificial Intelligence by : Yingying Chen
Download or read book Network Security Empowered by Artificial Intelligence written by Yingying Chen and published by Springer Nature. This book was released on with total page 443 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Adversarial Learning and Secure AI by : David J. Miller
Download or read book Adversarial Learning and Secure AI written by David J. Miller and published by Cambridge University Press. This book was released on 2023-08-31 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: Providing a logical framework for student learning, this is the first textbook on adversarial learning. It introduces vulnerabilities of deep learning, then demonstrates methods for defending against attacks and making AI generally more robust. To help students connect theory with practice, it explains and evaluates attack-and-defense scenarios alongside real-world examples. Feasible, hands-on student projects, which increase in difficulty throughout the book, give students practical experience and help to improve their Python and PyTorch skills. Book chapters conclude with questions that can be used for classroom discussions. In addition to deep neural networks, students will also learn about logistic regression, naïve Bayes classifiers, and support vector machines. Written for senior undergraduate and first-year graduate courses, the book offers a window into research methods and current challenges. Online resources include lecture slides and image files for instructors, and software for early course projects for students.
Book Synopsis On the Robustness of Neural Network: Attacks and Defenses by : Minhao Cheng
Download or read book On the Robustness of Neural Network: Attacks and Defenses written by Minhao Cheng and published by . This book was released on 2021 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks provide state-of-the-art results for most machine learning tasks. Unfortunately, neural networks are vulnerable to adversarial examples. That is, a slightly modified example could be easily generated and fool a well-trained image classifier based on deep neural networks (DNNs) with high confidence. This makes it difficult to apply neural networks in security-critical areas. To find such examples, we first introduce and define adversarial examples. In the first part, we then discuss how to build adversarial attacks in both image and discrete domains. For image classification, we introduce how to design an adversarial attacker in three different settings. Among them, we focus on the most practical setup for evaluating the adversarial robustness of a machine learning system with limited access: the hard-label black-box attack setting for generating adversarial examples, where limited model queries are allowed and only the decision is provided to a queried data input. For the discrete domain, we first talk about its difficulty and introduce how to conduct the adversarial attack on two applications. While crafting adversarial examples is an important technique to evaluate the robustness of DNNs, there is a huge need for improving the model robustness as well. Enhancing model robustness under new and even adversarial environments is a crucial milestone toward building trustworthy machine learning systems. In the second part, we talk about the methods to strengthen the model's adversarial robustness. We first discuss attack-dependent defense. Specifically, we first discuss one of the most effective methods for improving the robustness of neural networks: adversarial training and its limitations. We introduce a variant to overcome its problem. Then we take a different perspective and introduce attack-independent defense. We summarize the current methods and introduce a framework-based vicinal risk minimization. Inspired by the framework, we introduce self-progressing robust training. Furthermore, we discuss the robustness trade-off problem and introduce a hypothesis and propose a new method to alleviate it.
Book Synopsis Improved Methodology for Evaluating Adversarial Robustness in Deep Neural Networks by : Kyungmi Lee (S. M.)
Download or read book Improved Methodology for Evaluating Adversarial Robustness in Deep Neural Networks written by Kyungmi Lee (S. M.) and published by . This book was released on 2020 with total page 93 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 identify three common cases that lead to overestimation of accuracy against perturbed examples generated by bounded first-order attack methods: 1) the value of cross-entropy loss numerically becoming zero when using standard floating point representation, resulting in non-useful gradients; 2) innately non-differentiable functions in deep neural networks, such as Rectified Linear Unit (ReLU) activation and MaxPool operation, incurring “gradient masking” [2]; and 3) certain regularization methods used during training inducing the model to be less amenable to first-order approximation. We show that these phenomena exist in a wide range of deep neural networks, and that these phenomena are not limited to specific defense methods they have been previously investigated for. For each case, we propose compensation methods that either address sources of inaccurate gradient computation, such as numerical saturation for near zero values and non-differentiability, or reduce the total number of back-propagations for iterative attacks by approximating second-order information. These compensation methods can be combined with existing attack methods for a more precise empirical evaluation metric. We illustrate the impact of these three phenomena with examples of practical interest, such as benchmarking model capacity and regularization techniques for robustness. Furthermore, we show that the gap between adversarial accuracy and the guaranteed lower bound of robustness can be partially explained by these phenomena. Overall, our work shows that overestimated adversarial accuracy that is not indicative of robustness is prevalent even for conventionally trained deep neural networks, and highlights cautions of using empirical evaluation without guaranteed bounds.
Book Synopsis Evaluating and Understanding Adversarial Robustness in Deep Learning by : Jinghui Chen
Download or read book Evaluating and Understanding Adversarial Robustness in Deep Learning written by Jinghui Chen and published by . This book was released on 2021 with total page 175 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Adversarial Multimedia Forensics by : Ehsan Nowroozi
Download or read book Adversarial Multimedia Forensics written by Ehsan Nowroozi and published by Springer Nature. This book was released on with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Understanding and Interpreting Machine Learning in Medical Image Computing Applications by : Danail Stoyanov
Download or read book Understanding and Interpreting Machine Learning in Medical Image Computing Applications written by Danail Stoyanov and published by Springer. This book was released on 2018-10-23 with total page 149 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed joint proceedings of the First International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2018, the First International Workshop on Deep Learning Fails, DLF 2018, and the First International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 4 full MLCN papers, the 6 full DLF papers, and the 6 full iMIMIC papers included in this volume were carefully reviewed and selected. The MLCN contributions develop state-of-the-art machine learning methods such as spatio-temporal Gaussian process analysis, stochastic variational inference, and deep learning for applications in Alzheimer's disease diagnosis and multi-site neuroimaging data analysis; the DLF papers evaluate the strengths and weaknesses of DL and identify the main challenges in the current state of the art and future directions; the iMIMIC papers cover a large range of topics in the field of interpretability of machine learning in the context of medical image analysis.
