Adversarial Robustness For Machine Learning Models

Robust Machine Learning In Adversarial Setting With Provable Guarantee Book PDF
✏Book Title : Robust Machine Learning in Adversarial Setting with Provable Guarantee
✏Author : Yizhen Wang
✏Publisher :
✏Release Date : 2020
✏Pages : 178
✏ISBN : OCLC:1149141432
✏Available Language : English, Spanish, And French

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✏Robust Machine Learning in Adversarial Setting with Provable Guarantee Book Summary : 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.

📒Adversarial Machine Learning ✍ Yevgeniy Vorobeychik

Adversarial Machine Learning Book PDF
✏Book Title : Adversarial Machine Learning
✏Author : Yevgeniy Vorobeychik
✏Publisher : Morgan & Claypool Publishers
✏Release Date : 2018-08-08
✏Pages : 169
✏ISBN : 9781681733968
✏Available Language : English, Spanish, And French

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✏Adversarial Machine Learning Book Summary : 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 malicius objects they develop. The field of adversarial machine learning has emerged to study vulnerabilities of machine learning approaches in adversarial settings and to develop techniques to make learning robust to adversarial manipulation. This book provides a technical overview of this field. 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. 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.

Improved Methodology For Evaluating Adversarial Robustness In Deep Neural Networks Book PDF
✏Book Title : Improved Methodology for Evaluating Adversarial Robustness in Deep Neural Networks
✏Author : Kyungmi Lee (S. M.)
✏Publisher :
✏Release Date : 2020
✏Pages : 93
✏ISBN : OCLC:1192484009
✏Available Language : English, Spanish, And French

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✏Improved Methodology for Evaluating Adversarial Robustness in Deep Neural Networks Book Summary : 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.

Intelligent Systems And Applications Book PDF
✏Book Title : Intelligent Systems and Applications
✏Author : Kohei Arai
✏Publisher : Springer Nature
✏Release Date :
✏Pages :
✏ISBN : 9783030551872
✏Available Language : English, Spanish, And French

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✏Intelligent Systems and Applications Book Summary :

Machine Learning And Knowledge Discovery In Databases Book PDF
✏Book Title : Machine Learning and Knowledge Discovery in Databases
✏Author : Peggy Cellier
✏Publisher : Springer Nature
✏Release Date : 2020-03-27
✏Pages : 679
✏ISBN : 9783030438234
✏Available Language : English, Spanish, And French

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✏Machine Learning and Knowledge Discovery in Databases Book Summary : 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 WĂŒrzburg, 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 Machine Learning and Artificial Intelligence and eXplainable Knowledge Discovery in Data Mining, AIMLAI-XKDD 2019; Workshop on Decentralized Machine Learning at the Edge, DMLE 2019; Workshop on Advances in Managing and Mining Large Evolving Graphs, LEG 2019; Workshop on Data and Machine Learning Advances with Multiple Views; Workshop on New Trends in Representation Learning with Knowledge Graphs; Workshop on Data Science for Social Good, SoGood 2019; Workshop on Knowledge Discovery and User Modelling for Smart Cities, UMCIT 2019; Workshop on Data Integration and Applications Workshop, DINA 2019; Workshop on Machine Learning for Cybersecurity, MLCS 2019; Workshop on Sports Analytics: Machine Learning and Data Mining for Sports Analytics, MLSA 2019; Workshop on Categorising Different Types of Online Harassment Languages in Social Media; Workshop on IoT Stream for Data Driven Predictive Maintenance, IoTStream 2019; Workshop on Machine Learning and Music, MML 2019; Workshop on Large-Scale Biomedical Semantic Indexing and Question Answering, BioASQ 2019.

Science Of Cyber Security Book PDF
✏Book Title : Science of Cyber Security
✏Author : Feng Liu
✏Publisher : Springer Nature
✏Release Date : 2020-01-11
✏Pages : 382
✏ISBN : 9783030346379
✏Available Language : English, Spanish, And French

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✏Science of Cyber Security Book Summary : 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 Book PDF
✏Book Title : Engineering Dependable and Secure Machine Learning Systems
✏Author : Onn Shehory
✏Publisher : Springer Nature
✏Release Date : 2020-11-07
✏Pages : 141
✏ISBN : 9783030621445
✏Available Language : English, Spanish, And French

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✏Engineering Dependable and Secure Machine Learning Systems Book Summary : 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 Book PDF
✏Book Title : Towards Robust Deep Neural Networks
✏Author : Andras Rozsa
✏Publisher :
✏Release Date : 2018
✏Pages : 150
✏ISBN : OCLC:1127912167
✏Available Language : English, Spanish, And French

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✏Towards Robust Deep Neural Networks Book Summary : 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 designing and training machine learning models that perform extremely well, the intriguing discovery of adversarial examples challenges our understanding of these models and raises questions about their real-world applications. That is, vulnerable machine learning models misclassify examples that are indistinguishable from correctly classified examples by human observers. Furthermore, in many cases a variety of machine learning models having different architectures and/or trained on different subsets of training data misclassify the same adversarial example formed by an imperceptibly small perturbation. In this dissertation, we mainly focus on adversarial examples and closely related research areas such as quantifying the quality of adversarial examples in terms of human perception, proposing algorithms for generating adversarial examples, and analyzing the cross-model generalization properties of such examples. We further explore the robustness of facial attribute recognition and biometric face recognition systems to adversarial perturbations, and also investigate how to alleviate the intriguing properties of machine learning models.

Robust Machine Learning Algorithms And Systems For Detection And Mitigation Of Adversarial Attacks And Anomalies Book PDF
✏Book Title : Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies
✏Author : National Academies of Sciences, Engineering, and Medicine
✏Publisher : National Academies Press
✏Release Date : 2019-08-22
✏Pages : 82
✏ISBN : 9780309496094
✏Available Language : English, Spanish, And French

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✏Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies Book Summary : The Intelligence Community Studies Board (ICSB) of the National Academies of Sciences, Engineering, and Medicine convened a workshop on December 11ñ€"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.

Machine Learning In Adversarial Settings Book PDF
✏Book Title : Machine Learning in Adversarial Settings
✏Author : Hossein Hosseini
✏Publisher :
✏Release Date : 2019
✏Pages : 111
✏ISBN : OCLC:1128026840
✏Available Language : English, Spanish, And French

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✏Machine Learning in Adversarial Settings Book Summary : 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. Learning models are subject to attacks at both training and test phases. The main threat at test time is evasion attack, in which the attacker subtly modifies input data such that a human observer would perceive the original content, but the model generates different outputs. Such inputs, known as adversarial examples, has been used to attack voice interfaces, face-recognition systems and text classifiers. The goal of this dissertation is to investigate the test-time vulnerabilities of machine learning systems in adversarial settings and develop robust defensive mechanisms. The dissertation covers two classes of models, 1) commercial ML products developed by Google, namely Perspective, Cloud Vision, and Cloud Video Intelligence APIs, and 2) state-of-the-art image classification algorithms. In both cases, we propose novel test-time attack algorithms and also present defense methods against such attacks.

Artificial Neural Networks And Machine Learning Icann 2020 Book PDF
✏Book Title : Artificial Neural Networks and Machine Learning ICANN 2020
✏Author : Igor Farkaơ
✏Publisher : Springer Nature
✏Release Date :
✏Pages :
✏ISBN : 9783030616090
✏Available Language : English, Spanish, And French

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✏Artificial Neural Networks and Machine Learning ICANN 2020 Book Summary :

Reliable Machine Learning Via Distributional Robustness Book PDF
✏Book Title : Reliable Machine Learning Via Distributional Robustness
✏Author : Hongseok Namkoong
✏Publisher :
✏Release Date : 2019
✏Pages :
✏ISBN : OCLC:1114610895
✏Available Language : English, Spanish, And French

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✏Reliable Machine Learning Via Distributional Robustness Book Summary : 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 explicitly protect against potential shifts in the data-generating distribution. Instead of doing well just on average, distributionally robust methods learn models that can do well on a range of scenarios that are different to the training distribution. In the first part of thesis, we show that robustness to small perturbations in the data allows better generalization by optimally trading between approximation and estimation error. We show that robust solutions provide asymptotically exact confidence intervals and finite-sample guarantees for stochastic optimization problems. In the second part of the thesis, we focus on notions of distributional robustness that correspond to uniform performance across different subpopulations. We build procedures that balance tail-performance alongside classical notions of average performance. To trade these multiple goals \emph{optimally}, we show fundamental trade-offs (lower bounds), and develop efficient procedures that achieve these limits (upper bounds). Then, we extend our formulation to study partial covariate shifts, where we are interested in marginal distributional shifts on a subset of the feature vector. We provide convex procedures for these robust formulations, and characterize their non-asymptotic convergence properties. In the final part of the thesis, we develop and analyze distributionally robust approaches using Wasserstein distances, which allows models to generalize to distributions that have different support than the training distribution. We show that for smooth neural networks, our robust procedure guarantees performance under imperceptible adversarial perturbations. Extending such notions to protect against distributions defined on learned feature spaces, we show these models can also improve performance across unseen domains.

Strengthening Deep Neural Networks Book PDF
✏Book Title : Strengthening Deep Neural Networks
✏Author : Katy Warr
✏Publisher : O'Reilly Media
✏Release Date : 2019-07-03
✏Pages : 246
✏ISBN : 9781492044925
✏Available Language : English, Spanish, And French

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✏Strengthening Deep Neural Networks Book Summary : 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

Characterizing The Limits And Defenses Of Machine Learning In Adversarial Settings Book PDF
✏Book Title : Characterizing the Limits and Defenses of Machine Learning in Adversarial Settings
✏Author : Nicolas Papernot
✏Publisher :
✏Release Date : 2018
✏Pages :
✏ISBN : OCLC:1038418985
✏Available Language : English, Spanish, And French

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✏Characterizing the Limits and Defenses of Machine Learning in Adversarial Settings Book Summary : Advances in machine learning (ML) in recent years have enabled a dizzying array of applications such as object recognition, autonomous systems, security diagnostics, and playing the game of Go. Machine learning is not only a new paradigm for building software and systems, it is bringing social disruption at scale. There is growing recognition that ML exposes new vulnerabilities in software systems, yet the technical communitys understanding of the nature and extent of these vulnerabilities remains limited. In this thesis, I focus my study on the integrity of ML models. Integrity refers here to the faithfulness of model predictions with respect to an expected outcome. This property is at the core of traditional machine learning evaluation, as demonstrated by the pervasiveness of metrics such as accuracy among practitioners. A large fraction of ML techniques were designed for benign execution environments. Yet, the presence of adversaries may invalidate some of these underlying assumptions by forcing a mismatch between the distributions on which the model is trained and tested. As ML is increasingly applied and being relied on for decision-making in critical applications like transportation or energy, the models produced are becoming a target for adversaries who have a strong incentive to force ML to mispredict. I explore the space of attacks against ML integrity at test time. Given full or limited access to a trained model, I devise strategies that modify the test data to create a worst-case drift between the training and test distributions. The implications of this part of my research is that an adversary with very weak access to a system, and little knowledge about the ML techniques it deploys, can nevertheless mount powerful attacks against such systems as long as she has the capability of interacting with it as an oracle: i.e., send inputs of the adversarys choice and observe the ML prediction. This systematic exposition of the poor generalization of ML models indicates the lack of reliable confidence estimates when the model is making predictions far from its training data. Hence, my efforts to increase the robustness of models to these adversarial manipulations strive to decrease the confidence of predictions made far from the training distribution. Informed by my progress on attacks operating in the black-box threat model, I first identify limitations to two defenses: defensive distillation and adversarial training. I then describe recent defensive efforts addressing these shortcomings. To this end, I introduce the Deep k-Nearest Neighbors classifier, which augments deep neural networks with an integrity check at test time. The approach compares internal representations produced by the deep neural network on test data with the ones learned on its training points. Using the labels of training points whose representations neighbor the test input across the deep neural networks layers, I estimate the nonconformity of the prediction with respect to the models training data. An application of conformal prediction methodology then paves the way for more reliable estimates of the models prediction credibility, i.e., how well the prediction is supported by training data. In turn, we distinguish legitimate test data with high credibility from adversarial data with low credibility. This research calls for future efforts to investigate the robustness of individual layers of deep neural networks rather than treating the model as a black-box. This aligns well with the modular nature of deep neural networks, which orchestrate simple computations to model complex functions. This also allows us to draw connections to other areas like interpretability in ML, which seeks to answer the question of: How can we provide an explanation for the model prediction to a human? Another by-product of this research direction is that I better distinguish vulnerabilities of ML models that are a consequence of the ML algorithms from those that can be explained by artifacts in the data.

Artificial Neural Networks And Machine Learning Icann 2019 Image Processing Book PDF
✏Book Title : Artificial Neural Networks and Machine Learning ICANN 2019 Image Processing
✏Author : Igor V. Tetko
✏Publisher : Springer Nature
✏Release Date : 2019-11-03
✏Pages : 733
✏ISBN : 9783030305086
✏Available Language : English, Spanish, And French

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✏Artificial Neural Networks and Machine Learning ICANN 2019 Image Processing Book Summary : 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.

Advances In Artificial Intelligence Book PDF
✏Book Title : Advances in Artificial Intelligence
✏Author : Cyril Goutte
✏Publisher : Springer Nature
✏Release Date : 2020-05-05
✏Pages : 572
✏ISBN : 9783030473587
✏Available Language : English, Spanish, And French

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✏Advances in Artificial Intelligence Book Summary : This book constitutes the refereed proceedings of the 33rd Canadian Conference on Artificial Intelligence, Canadian AI 2020, which was planned to take place in Ottawa, ON, Canada. Due to the COVID-19 pandemic, however, it was held virtually during May 13–15, 2020. The 31 regular papers and 24 short papers presented together with 4 Graduate Student Symposium papers were carefully reviewed and selected from a total of 175 submissions. The selected papers cover a wide range of topics, including machine learning, pattern recognition, natural language processing, knowledge representation, cognitive aspects of AI, ethics of AI, and other important aspects of AI research.

Advanced Deep Learning With Python Book PDF
✏Book Title : Advanced Deep Learning with Python
✏Author : Ivan Vasilev
✏Publisher : Packt Publishing Ltd
✏Release Date : 2019-12-12
✏Pages : 468
✏ISBN : 9781789952711
✏Available Language : English, Spanish, And French

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✏Advanced Deep Learning with Python Book Summary : Gain expertise in advanced deep learning domains such as neural networks, meta-learning, graph neural networks, and memory augmented neural networks using the Python ecosystem Key Features Get to grips with building faster and more robust deep learning architectures Investigate and train convolutional neural network (CNN) models with GPU-accelerated libraries such as TensorFlow and PyTorch Apply deep neural networks (DNNs) to computer vision problems, NLP, and GANs Book Description In order to build robust deep learning systems, you’ll need to understand everything from how neural networks work to training CNN models. In this book, you’ll discover newly developed deep learning models, methodologies used in the domain, and their implementation based on areas of application. You’ll start by understanding the building blocks and the math behind neural networks, and then move on to CNNs and their advanced applications in computer vision. You'll also learn to apply the most popular CNN architectures in object detection and image segmentation. Further on, you’ll focus on variational autoencoders and GANs. You’ll then use neural networks to extract sophisticated vector representations of words, before going on to cover various types of recurrent networks, such as LSTM and GRU. You’ll even explore the attention mechanism to process sequential data without the help of recurrent neural networks (RNNs). Later, you’ll use graph neural networks for processing structured data, along with covering meta-learning, which allows you to train neural networks with fewer training samples. Finally, you’ll understand how to apply deep learning to autonomous vehicles. By the end of this book, you’ll have mastered key deep learning concepts and the different applications of deep learning models in the real world. What you will learn Cover advanced and state-of-the-art neural network architectures Understand the theory and math behind neural networks Train DNNs and apply them to modern deep learning problems Use CNNs for object detection and image segmentation Implement generative adversarial networks (GANs) and variational autoencoders to generate new images Solve natural language processing (NLP) tasks, such as machine translation, using sequence-to-sequence models Understand DL techniques, such as meta-learning and graph neural networks Who this book is for This book is for data scientists, deep learning engineers and researchers, and AI developers who want to further their knowledge of deep learning and build innovative and unique deep learning projects. Anyone looking to get to grips with advanced use cases and methodologies adopted in the deep learning domain using real-world examples will also find this book useful. Basic understanding of deep learning concepts and working knowledge of the Python programming language is assumed.

Neural Information Processing Book PDF
✏Book Title : Neural Information Processing
✏Author : Tom Gedeon
✏Publisher : Springer Nature
✏Release Date : 2019-12-06
✏Pages : 782
✏ISBN : 9783030368081
✏Available Language : English, Spanish, And French

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✏Neural Information Processing Book Summary : The two-volume set CCIS 1142 and 1143 constitutes thoroughly refereed contributions presented at the 26th International Conference on Neural Information Processing, ICONIP 2019, held in Sydney, Australia, in December 2019. For ICONIP 2019 a total of 345 papers was carefully reviewed and selected for publication out of 645 submissions. The 168 papers included in this volume set were organized in topical sections as follows: adversarial networks and learning; convolutional neural networks; deep neural networks; embeddings and feature fusion; human centred computing; human centred computing and medicine; human centred computing for emotion; hybrid models; image processing by neural techniques; learning from incomplete data; model compression and optimization; neural network applications; neural network models; semantic and graph based approaches; social network computing; spiking neuron and related models; text computing using neural techniques; time-series and related models; and unsupervised neural models.

Machine Learning Techniques For Forensic Camera Model Identification And Anti Forensic Attacks Book PDF
✏Book Title : Machine Learning Techniques for Forensic Camera Model Identification and Anti forensic Attacks
✏Author : Chen Chen
✏Publisher :
✏Release Date : 2019
✏Pages : 208
✏ISBN : OCLC:1149063271
✏Available Language : English, Spanish, And French

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✏Machine Learning Techniques for Forensic Camera Model Identification and Anti forensic Attacks Book Summary : The goal of camera model identification is to determine the manufacturer and model of an image's source camera. Camera model identification is an important task in multimedia forensics because it helps verify the origin of an image and uncover possible image forgeries. Forensic camera model identification is generally performed by searching an image for model-specific traces left by a camera's internal image processing components. Many techniques, including recent data-driven deep learning algorithms, have been developed to perform camera model identification. In the meantime, forensic researchers have discovered that existing camera model identification algorithms can be maliciously attacked by altering images without leaving visually distinguishable artifacts. These anti-forensic attacks arouse concerns about the robustness of camera model identification techniques and urge the need for effective defense strategies. In this thesis, we propose new algorithms to perform forensic camera model identification, and new anti-forensic attacks. We first introduce a highly accurate and robust camera model identification framework developed by fully exploiting demosaicing traces left by cameras' internal demosaicing process. In light of the complexity of demosaicing traces, we build an ensemble of statistical models to capture diverse demosaicing information in the form of content-dependent color value correlations. Diversity among these statistical models is critical for each model to capture a unique set of color correlations introduced by the demosaicing process. We obtain a diverse set of linear and non-linear demosaicing residuals and extract both intra-channel and inter-channel color correlations following a variety of geometric structures. The ensemble of collect diverse color correlations forms a comprehensive representation of the sophisticated demosaicing process inside a camera. This proposed framework not only achieves high camera model identification accuracy, but more importantly, it is robust to image post-processing operations and anti-forensic camera model attacks. Given recent popularity of deep learning algorithms, forensic researchers have started to build deep neural networks, especially convolutional neural networks, to perform camera model identification. In this thesis, we investigate the robustness of deep learning based camera model identification algorithms by developing anti-forensic camera model attacks to expose vulnerability of these algorithms. We propose a generative adversarial attack to perform targeted camera model falsification. Given full access to the camera model identification networks, this attack has been proven to be able to falsify camera models of images from arbitrary sources. Under black-box scenarios where no information about the camera model identification networks is available, we train a substitute network which mimics the camera model identification networks and provides gradient information to craft adversarial images.

Machine Learning For Speaker Recognition Book PDF
✏Book Title : Machine Learning for Speaker Recognition
✏Author : Man-Wai Mak
✏Publisher : Cambridge University Press
✏Release Date : 2020-09-30
✏Pages : 336
✏ISBN : 9781108428125
✏Available Language : English, Spanish, And French

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✏Machine Learning for Speaker Recognition Book Summary : Learn fundamental and advanced machine learning techniques for robust speaker recognition and domain adaptation with this useful toolkit.

Machine Learning And Knowledge Discovery In Databases Book PDF
✏Book Title : Machine Learning and Knowledge Discovery in Databases
✏Author : Michele Berlingerio
✏Publisher : Springer
✏Release Date : 2019-01-17
✏Pages : 740
✏ISBN : 9783030109257
✏Available Language : English, Spanish, And French

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✏Machine Learning and Knowledge Discovery in Databases Book Summary : The three volume proceedings LNAI 11051 – 11053 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2018, held in Dublin, Ireland, in September 2018. The total of 131 regular papers presented in part I and part II was carefully reviewed and selected from 535 submissions; there are 52 papers in the applied data science, nectar and demo track. The contributions were organized in topical sections named as follows: Part I: adversarial learning; anomaly and outlier detection; applications; classification; clustering and unsupervised learning; deep learningensemble methods; and evaluation. Part II: graphs; kernel methods; learning paradigms; matrix and tensor analysis; online and active learning; pattern and sequence mining; probabilistic models and statistical methods; recommender systems; and transfer learning. Part III: ADS data science applications; ADS e-commerce; ADS engineering and design; ADS financial and security; ADS health; ADS sensing and positioning; nectar track; and demo track.

Security Of Deep Reinforcement Learning Book PDF
✏Book Title : Security of Deep Reinforcement Learning
✏Author : Vahid Behzadan
✏Publisher :
✏Release Date : 2019
✏Pages :
✏ISBN : OCLC:1117710669
✏Available Language : English, Spanish, And French

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✏Security of Deep Reinforcement Learning Book Summary : Since the inception of Deep Reinforcement Learning (DRL) algorithms, there has been a growing interest from both the research and the industrial communities in the promising potentials of this paradigm. The list of current and envisioned applications of deep RL ranges from autonomous navigation and robotics to control applications in the critical infrastructure, air traffic control, defense technologies, and cybersecurity. While the landscape of opportunities and the advantages of deep RL algorithms are justifiably vast, the security risks and issues in such algorithms remain largely unexplored. It has been shown that DRL algorithms are very brittle in terms of their sensitivity to small perturbations of their observations of the state. Furthermore, recent reports demonstrate that such perturbations can be applied by an adversary to manipulate the performance and behavior of DRL agents. To address such problems, this dissertation aims to advance the current state of the art in three separate, but interdependent directions. First, I build on the recent developments in adversarial machine learning and robust reinforcement learning to develop techniques and metrics for evaluating the resilience and robustness of DRL agents to adversarial perturbations applied to the observations of state transitions. A main objective of this task is to disentangle the vulnerabilities in the learned representation of state from those that stem from the sensitivity of DRL policies to changes in transition dynamics. A further objective is to investigate evaluation methods that are independent of attack techniques and their specific parameters. Accordingly, I develop two DRL-based algorithms that enable the quantitative measurement and benchmarking of worst-case resilience and robustness in DRL policies. Second, I present an analysis of \emph{adversarial training} as a solution to the brittleness of Deep Q-Network (DQN) policies, and investigate the impact of hyperparameters on the training-time resilience of policies. I also propose a new exploration mechanism for sample-efficient adversarial training of DRL agents. Third, I address the previously unexplored problem of model extraction attacks on DRL agents. Accordingly, I demonstrate that imitation learning techniques can be used to effectively replicate a DRL policy from observations of its behavior. Moreover, I establish that the replicated policies can be used to launch effective black-box adversarial attacks through the transferability of adversarial examples. Lastly, I address the problem of detecting replicated models by developing a novel technique for embedding sequential watermarks in DRL policies. The dissertation concludes with remarks on the remaining challenges and future directions of research in emerging domain of DRL security.

Evaluation And Design Of Robust Neural Network Defenses Book PDF
✏Book Title : Evaluation and Design of Robust Neural Network Defenses
✏Author : Nicholas Carlini
✏Publisher :
✏Release Date : 2018
✏Pages : 138
✏ISBN : OCLC:1083588772
✏Available Language : English, Spanish, And French

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✏Evaluation and Design of Robust Neural Network Defenses Book Summary : 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.

📒Dataset Shift In Machine Learning ✍ Joaquin Quiñonero-Candela

Dataset Shift In Machine Learning Book PDF
✏Book Title : Dataset Shift in Machine Learning
✏Author : Joaquin Quiñonero-Candela
✏Publisher : Neural Information Processing
✏Release Date : 2009
✏Pages : 229
✏ISBN : UOM:39015080846309
✏Available Language : English, Spanish, And French

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✏Dataset Shift in Machine Learning Book Summary : An overview of recent efforts in the machine learning community to deal with dataset and covariate shift, which occurs when test and training inputs and outputs have different distributions. Dataset shift is a common problem in predictive modeling that occurs when the joint distribution of inputs and outputs differs between training and test stages. Covariate shift, a particular case of dataset shift, occurs when only the input distribution changes. Dataset shift is present in most practical applications, for reasons ranging from the bias introduced by experimental design to the irreproducibility of the testing conditions at training time. (An example is -email spam filtering, which may fail to recognize spam that differs in form from the spam the automatic filter has been built on.) Despite this, and despite the attention given to the apparently similar problems of semi-supervised learning and active learning, dataset shift has received relatively little attention in the machine learning community until recently. This volume offers an overview of current efforts to deal with dataset and covariate shift. The chapters offer a mathematical and philosophical introduction to the problem, place dataset shift in relationship to transfer learning, transduction, local learning, active learning, and semi-supervised learning, provide theoretical views of dataset and covariate shift (including decision theoretic and Bayesian perspectives), and present algorithms for covariate shift. Contributors Shai Ben-David, Steffen Bickel, Karsten Borgwardt, Michael BrĂŒckner, David Corfield, Amir Globerson, Arthur Gretton, Lars Kai Hansen, Matthias Hein, Jiayuan Huang, Choon Hui Teo, Takafumi Kanamori, Klaus-Robert MĂŒller, Sam Roweis, Neil Rubens, Tobias Scheffer, Marcel Schmittfull, Bernhard Schölkopf Hidetoshi Shimodaira, Alex Smola, Amos Storkey, Masashi Sugiyama

A Dynamic Adversarial Mining Approach To The Security Of Machine Learning Book PDF
✏Book Title : A Dynamic adversarial Mining Approach to the Security of Machine Learning
✏Author :
✏Publisher :
✏Release Date : 2018
✏Pages :
✏ISBN : OCLC:1051988375
✏Available Language : English, Spanish, And French

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✏A Dynamic adversarial Mining Approach to the Security of Machine Learning Book Summary : Abstract : Operating in a dynamic real‐world environment requires a forward thinking and adversarial aware design for classifiers beyond fitting the model to the training data. In such scenarios, it is necessary to make classifiers such that they are: (a) harder to evade, (b) easier to detect changes in the data distribution over time, and (c) be able to retrain and recover from model degradation. While most works in the security of machine learning have concentrated on the evasion resistance problem (a), there is little work in the areas of reacting to attacks (b) and (c). Additionally, while streaming data research concentrates on the ability to react to changes to the data distribution, they often take an adversarial agnostic view of the security problem. This makes them vulnerable to adversarial activity, which is aimed toward evading the concept drift detection mechanism itself. In this paper, we analyze the security of machine learning from a dynamic and adversarial aware perspective. The existing techniques of restrictive one‐class classifier models, complex learning‐based ensemble models, and randomization‐based ensemble models are shown to be myopic as they approach security as a static task. These methodologies are ill suited for a dynamic environment, as they leak excessive information to an adversary who can subsequently launch attacks which are indistinguishable from the benign data. Based on empirical vulnerability analysis against a sophisticated adversary, a novel feature importance hiding approach for classifier design is proposed. The proposed design ensures that future attacks on classifiers can be detected and recovered from. The proposed work provides motivation, by serving as a blueprint, for future work in the area of dynamic‐adversarial mining, which combines lessons learned from streaming data mining, adversarial learning, and cybersecurity. This article is categorized under: Technologies > Machine Learning Technologies > Classification Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining Abstract : Classifiers operating in the real world are prone to adversarial evasion at test time. Any practical robust classifier needs to be prepared for such attacks and needs to take proactive steps to ensure it is one step ahead of the attacker.

Building And Using Robust Representations In Image Classification Book PDF
✏Book Title : Building and Using Robust Representations in Image Classification
✏Author : Brandon Vanhuy Tran
✏Publisher :
✏Release Date : 2020
✏Pages : 199
✏ISBN : OCLC:1197636585
✏Available Language : English, Spanish, And French

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✏Building and Using Robust Representations in Image Classification Book Summary : One of the major appeals of the deep learning paradigm is the ability to learn high-level feature representations of complex data. These learned representations obviate manual data pre-processing, and are versatile enough to generalize across tasks. However, they are not yet capable of fully capturing abstract, meaningful features of the data. For instance, the pervasiveness of adversarial examples--small perturbations of correctly classified inputs causing model misclassification--is a prominent indication of such shortcomings. The goal of this thesis is to work towards building learned representations that are more robust and human-aligned. To achieve this, we turn to adversarial (or robust) training, an optimization technique for training networks less prone to adversarial inputs. Typically, robust training is studied purely in the context of machine learning security (as a safeguard against adversarial examples)--in contrast, we will cast it as a means of enforcing an additional prior onto the model. Specifically, it has been noticed that, in a similar manner to the well-known convolutional or recurrent priors, the robust prior serves as a "bias" that restricts the features models can use in classification--it does not allow for any features that change upon small perturbations. We find that the addition of this simple prior enables a number of downstream applications, from feature visualization and manipulation to input interpolation and image synthesis. Most importantly, robust training provides a simple way of interpreting and understanding model decisions. Besides diagnosing incorrect classification, this also has consequences in the so-called "data poisoning" setting, where an adversary corrupts training samples with the hope of causing misbehaviour in the resulting model. We find that in many cases, the prior arising from robust training significantly helps in detecting data poisoning.

📒Prediction Games ✍ Michael BrĂŒckner

Prediction Games Book PDF
✏Book Title : Prediction Games
✏Author : Michael BrĂŒckner
✏Publisher : UniversitĂ€tsverlag Potsdam
✏Release Date : 2012
✏Pages : 121
✏ISBN : 9783869562032
✏Available Language : English, Spanish, And French

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✏Prediction Games Book Summary :

Deployable Machine Learning For Security Defense Book PDF
✏Book Title : Deployable Machine Learning for Security Defense
✏Author : Gang Wang
✏Publisher : Springer Nature
✏Release Date :
✏Pages :
✏ISBN : 9783030596217
✏Available Language : English, Spanish, And French

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✏Deployable Machine Learning for Security Defense Book Summary :

📒Succinct And Assured Machine Learning ✍ Bita Darvish Rouhani

Succinct And Assured Machine Learning Book PDF
✏Book Title : Succinct and Assured Machine Learning
✏Author : Bita Darvish Rouhani
✏Publisher :
✏Release Date : 2018
✏Pages : 204
✏ISBN : OCLC:1052622017
✏Available Language : English, Spanish, And French

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✏Succinct and Assured Machine Learning Book Summary : Contemporary datasets are rapidly growing in size and complexity. This wealth of data is providing a paradigm shift in various key sectors including defense, commercial, and personalized computing. Over the past decade, machine learning and related fields have made significant progress in designing rigorous algorithms with the goal of making sense of this large corpus of available data. Concerns over physical performance (runtime and energy consumption), reliability (safety), and ease-of-use, however, pose major roadblocks to the wider adoption of machine learning techniques. To address the aforementioned roadblocks, a popular recent line of research is focused on performance optimization and machine learning acceleration via hardware/software co-design and automation. This thesis advances the state-of-the-art in this growing field by advocating a holistic automated co-design approach which involves not only hardware and software but also the geometry of the data and learning model as well as the security requirements. My key contributions include: Co-optimizing graph traversal, data embedding, and resource allocation for succinct training and execution of Deep Learning (DL) models. The resource efficiency of my end-to-end automated solutions not only enables compact DL training/execution on edge devices but also facilitates further reduction of the training time and energy spent on cloud data servers. Characterizing and thwarting adversarial subspace for robust and assured execution of DL models. I build a holistic hardware/software/algorithm co-design that enables just-in-time defense against adversarial attacks. My proposed countermeasure is robust against the strongest adversarial attacks known to date without violating the real-time response requirement, which is crucial in sensitive applications such as autonomous vehicles/drones. Proposing the first efficient resource management framework that empowers coherent integration of robust digital watermarks/fingerprints into DL models. The embedded digital watermarks/fingerprints are robust to removal and transformation attacks and can be used for model protection against intellectual property infringement. Devising the first reconfigurable and provably-secure framework that simultaneously enables accurate and scalable DL execution on encrypted data. The proposed framework supports secure streaming-based DL computation on cloud servers equipped with FPGAs. Developing the first scalable framework that enables real-time approximation of multi-dimensional probability density functions for causal Bayesian analysis. The proposed solution adaptively fine-tunes the underlying latent variables to cope with the data dynamics as it evolves over time.

Artificial Neural Networks And Machine Learning Icann 2019 Text And Time Series Book PDF
✏Book Title : Artificial Neural Networks and Machine Learning ICANN 2019 Text and Time Series
✏Author : Igor V. Tetko
✏Publisher : Springer Nature
✏Release Date : 2019-11-02
✏Pages : 761
✏ISBN : 9783030304904
✏Available Language : English, Spanish, And French

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✏Artificial Neural Networks and Machine Learning ICANN 2019 Text and Time Series Book Summary : 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.