Theoretical Foundations of Adversarial Binary Detection PDF Download
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Author: Mauro Barni
Publisher:
ISBN: 9781680837643
Category : Technology & Engineering
Languages : en
Pages : 190
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Book Description
Author: Mauro Barni
Publisher:
ISBN: 9781680837643
Category : Technology & Engineering
Languages : en
Pages : 190
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Book Description
Author: Mauro Barni (Ph. D.)
Publisher:
ISBN: 9781680837650
Category : Electronic books
Languages : en
Pages : 172
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Book Description
This monograph, aimed at students, researchers and practitioners working in the application areas who want an accessible introduction to the theory behind Adversarial Binary Detection and the possible solutions to their particular problem.
Author: Charles A. Kamhoua
Publisher: John Wiley & Sons
ISBN: 1119723949
Category : Technology & Engineering
Languages : en
Pages : 546
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Book Description
GAME THEORY AND MACHINE LEARNING FOR CYBER SECURITY Move beyond the foundations of machine learning and game theory in cyber security to the latest research in this cutting-edge field In Game Theory and Machine Learning for Cyber Security, a team of expert security researchers delivers a collection of central research contributions from both machine learning and game theory applicable to cybersecurity. The distinguished editors have included resources that address open research questions in game theory and machine learning applied to cyber security systems and examine the strengths and limitations of current game theoretic models for cyber security. Readers will explore the vulnerabilities of traditional machine learning algorithms and how they can be mitigated in an adversarial machine learning approach. The book offers a comprehensive suite of solutions to a broad range of technical issues in applying game theory and machine learning to solve cyber security challenges. Beginning with an introduction to foundational concepts in game theory, machine learning, cyber security, and cyber deception, the editors provide readers with resources that discuss the latest in hypergames, behavioral game theory, adversarial machine learning, generative adversarial networks, and multi-agent reinforcement learning. Readers will also enjoy: A thorough introduction to game theory for cyber deception, including scalable algorithms for identifying stealthy attackers in a game theoretic framework, honeypot allocation over attack graphs, and behavioral games for cyber deception An exploration of game theory for cyber security, including actionable game-theoretic adversarial intervention detection against advanced persistent threats Practical discussions of adversarial machine learning for cyber security, including adversarial machine learning in 5G security and machine learning-driven fault injection in cyber-physical systems In-depth examinations of generative models for cyber security Perfect for researchers, students, and experts in the fields of computer science and engineering, Game Theory and Machine Learning for Cyber Security is also an indispensable resource for industry professionals, military personnel, researchers, faculty, and students with an interest in cyber security.
Author: Zheng Zhang
Publisher: Springer Nature
ISBN: 9819721121
Category : Content-based image retrieval
Languages : en
Pages : 212
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Book Description
This book introduces pioneering developments in binary representation learning on visual images, a state-of-the-art data transformation methodology within the fields of machine learning and multimedia. Binary representation learning, often known as learning to hash or hashing, excels in converting high-dimensional data into compact binary codes meanwhile preserving the semantic attributes and maintaining the similarity measurements. The book provides a comprehensive introduction to the latest research in hashing-based visual image retrieval, with a focus on binary representations. These representations are crucial in enabling fast and reliable feature extraction and similarity assessments on large-scale data. This book offers an insightful analysis of various research methodologies in binary representation learning for visual images, ranging from basis shallow hashing, advanced high-order similarity-preserving hashing, deep hashing, as well as adversarial and robust deep hashing techniques. These approaches can empower readers to proficiently grasp the fundamental principles of the traditional and state-of-the-art methods in binary representations, modeling, and learning. The theories and methodologies of binary representation learning expounded in this book will be beneficial to readers from diverse domains such as machine learning, multimedia, social network analysis, web search, information retrieval, data mining, and others.
Author: Aneesh Sreevallabh Chivukula
Publisher: Springer Nature
ISBN: 3030997723
Category : Computers
Languages : en
Pages : 316
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Book Description
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.
Author: Anthony D. Joseph
Publisher: Cambridge University Press
ISBN: 1107043468
Category : Computers
Languages : en
Pages : 341
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Book Description
This study allows readers to get to grips with the conceptual tools and practical techniques for building robust machine learning in the face of adversaries.
Author: Shai Shalev-Shwartz
Publisher: Cambridge University Press
ISBN: 1107057132
Category : Computers
Languages : en
Pages : 415
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Book Description
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Author: Cynthia Dwork
Publisher:
ISBN: 9781601988188
Category : Computers
Languages : en
Pages : 286
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Book Description
The problem of privacy-preserving data analysis has a long history spanning multiple disciplines. As electronic data about individuals becomes increasingly detailed, and as technology enables ever more powerful collection and curation of these data, the need increases for a robust, meaningful, and mathematically rigorous definition of privacy, together with a computationally rich class of algorithms that satisfy this definition. Differential Privacy is such a definition. The Algorithmic Foundations of Differential Privacy starts out by motivating and discussing the meaning of differential privacy, and proceeds to explore the fundamental techniques for achieving differential privacy, and the application of these techniques in creative combinations, using the query-release problem as an ongoing example. A key point is that, by rethinking the computational goal, one can often obtain far better results than would be achieved by methodically replacing each step of a non-private computation with a differentially private implementation. Despite some powerful computational results, there are still fundamental limitations. Virtually all the algorithms discussed herein maintain differential privacy against adversaries of arbitrary computational power -- certain algorithms are computationally intensive, others are efficient. Computational complexity for the adversary and the algorithm are both discussed. The monograph then turns from fundamentals to applications other than query-release, discussing differentially private methods for mechanism design and machine learning. The vast majority of the literature on differentially private algorithms considers a single, static, database that is subject to many analyses. Differential privacy in other models, including distributed databases and computations on data streams, is discussed. The Algorithmic Foundations of Differential Privacy is meant as a thorough introduction to the problems and techniques of differential privacy, and is an invaluable reference for anyone with an interest in the topic.
Author: Daniel Asante Otchere
Publisher: CRC Press
ISBN: 1003860192
Category : Science
Languages : en
Pages : 322
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Book Description
This book covers unsupervised learning, supervised learning, clustering approaches, feature engineering, explainable AI and multioutput regression models for subsurface engineering problems. Processing voluminous and complex data sets are the primary focus of the field of machine learning (ML). ML aims to develop data-driven methods and computational algorithms that can learn to identify complex and non-linear patterns to understand and predict the relationships between variables by analysing extensive data. Although ML models provide the final output for predictions, several steps need to be performed to achieve accurate predictions. These steps, data pre-processing, feature selection, feature engineering and outlier removal, are all contained in this book. New models are also developed using existing ML architecture and learning theories to improve the performance of traditional ML models and handle small and big data without manual adjustments. This research-oriented book will help subsurface engineers, geophysicists, and geoscientists become familiar with data science and ML advances relevant to subsurface engineering. Additionally, it demonstrates the use of data-driven approaches for salt identification, seismic interpretation, estimating enhanced oil recovery factor, predicting pore fluid types, petrophysical property prediction, estimating pressure drop in pipelines, bubble point pressure prediction, enhancing drilling mud loss, smart well completion and synthetic well log predictions.
Author: Baochang Zhang
Publisher: CRC Press
ISBN: 1003816851
Category : Computers
Languages : en
Pages : 393
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Book Description
Deep learning has achieved impressive results in image classification, computer vision, and natural language processing. To achieve better performance, deeper and wider networks have been designed, which increase the demand for computational resources. The number of floatingpoint operations (FLOPs) has increased dramatically with larger networks, and this has become an obstacle for convolutional neural networks (CNNs) being developed for mobile and embedded devices. In this context, Binary Neural Networks: Algorithms, Architectures, and Applications will focus on CNN compression and acceleration, which are important for the research community. We will describe numerous methods, including parameter quantization, network pruning, low-rank decomposition, and knowledge distillation. More recently, to reduce the burden of handcrafted architecture design, neural architecture search (NAS) has been used to automatically build neural networks by searching over a vast architecture space. Our book will also introduce NAS and binary NAS and its superiority and state-of-the-art performance in various applications, such as image classification and object detection. We also describe extensive applications of compressed deep models on image classification, speech recognition, object detection, and tracking. These topics can help researchers better understand the usefulness and the potential of network compression on practical applications. Moreover, interested readers should have basic knowledge of machine learning and deep learning to better understand the methods described in this book. Key Features • Reviews recent advances in CNN compression and acceleration • Elaborates recent advances on binary neural network (BNN) technologies • Introduces applications of BNN in image classification, speech recognition, object detection, and more Baochang Zhang is a full professor with the Institute of Artificial Intelligence, Beihang University, Beijing, China. He was selected by the Program for New Century Excellent Talents in the University of Ministry of Education of China, chosen as the Academic Advisor of the Deep Learning Lab of Baidu Inc., and was honored as a Distinguished Researcher of Beihang Hangzhou Institute in Zhejiang Province. His research interests include explainable deep learning, computer vision, and pattern recognition. His HGPP and LDP methods were state-of-the-art feature descriptors, with 1234 and 768 Google Scholar citations, respectively, and both “Test-of-Time” works. His team’s 1-bit methods achieved the best performance on ImageNet. His group also won the ECCV 2020 Tiny Object Detection, COCO Object Detection, and ICPR 2020 Pollen recognition challenges. Sheng Xu received a BE in automotive engineering from Beihang University, Beijing, China. He has a PhD and is currently at the School of Automation Science and Electrical Engineering, Beihang University, specializing in computer vision, model quantization, and compression. He has made significant contributions to the field and has published about a dozen papers as the first author in top-tier conferences and journals such as CVPR, ECCV, NeurIPS, AAAI, BMVC, IJCV, and ACM TOMM. Notably, he has 4 papers selected as oral or highlighted presentations by these prestigious conferences. Furthermore, Dr. Xu actively participates in the academic community as a reviewer for various international journals and conferences, including CVPR, ICCV, ECCV, NeurIPS, ICML, and IEEE TCSVT. His expertise has also led to his group’s victory in the ECCV 2020 Tiny Object Detection Challenge. Mingbao Lin finished his MS-PhD study and obtained a PhD in intelligence science and technology from Xiamen University, Xiamen, China in 2022. In 2016, he received a BS from Fuzhou University, Fuzhou, China. He is currently a senior researcher with the Tencent Youtu Lab, Shanghai, China. His publications on top-tier conferences/journals include: IEEE TPAMI, IJCV, IEEE TIP, IEEE TNNLS, CVPR, NeurIPS, AAAI, IJCAI, ACM MM, and more. His current research interests include developing an efficient vision model, as well as information retrieval. Tiancheng Wang received a BE in automation from Beihang University, Beijing, China. He is currently pursuing a PhD with the Institute of Artificial Intelligence, Beihang University. During his undergraduate studies, he was given the Merit Student Award for several consecutive years, and has received various scholarships including academic excellence and academic competitions scholarships. He was involved in several AI projects including behavior detection and intention understanding research and unmanned air-based vision platform, and more. Now his current research interests include deep learning and network compression; his goal is to explore a high energy-saving model and drive the deployment of neural networks in embedded devices. Dr. David Doermann is a professor of empire innovation at the University at Buffalo (UB), New York, US, and the director of the University at Buffalo Artificial Intelligence Institute. Prior to coming to UB, he was a program manager at the Defense Advanced Research Projects Agency (DARPA) where he developed, selected, and oversaw approximately $150 million in research and transition funding in the areas of computer vision, human language technologies, and voice analytics. He coordinated performers on all projects, orchestrating consensus, evaluating cross team management, and overseeing fluid program objectives.
