Adversarial Robustness for Machine Learning Book

Adversarial Robustness for Machine Learning


  • Author : Pin-Yu Chen
  • Publisher : Academic Press
  • Release Date : 2022-08-20
  • Genre: Computers
  • Pages : 300
  • ISBN 10 : 9780128242575

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Adversarial Robustness for Machine Learning Excerpt :

Adversarial Robustness for Machine Learning summarizes the recent progress on this topic and introduces popular algorithms on adversarial attack, defense and veri?cation. Sections cover adversarial attack, veri?cation and defense, mainly focusing on image classi?cation applications which are the standard benchmark considered in the adversarial robustness community. Other sections discuss adversarial examples beyond image classification, other threat models beyond testing time attack, and applications on adversarial robustness. For researchers, this book provides a thorough literature review that summarizes latest progress in the area, which can be a good reference for conducting future research. In addition, the book can also be used as a textbook for graduate courses on adversarial robustness or trustworthy machine learning. While machine learning (ML) algorithms have achieved remarkable performance in many applications, recent studies have demonstrated their lack of robustness against adversarial disturbance. The lack of robustness brings security concerns in ML models for real applications such as self-driving cars, robotics controls and healthcare systems. Summarizes the whole field of adversarial robustness for Machine learning models Provides a clearly explained, self-contained reference Introduces formulations, algorithms and intuitions Includes applications based on adversarial robustness

Adversarial Machine Learning Book

Adversarial Machine Learning


  • Author : Yevgeniy Vorobeychik
  • Publisher : Morgan & Claypool Publishers
  • Release Date : 2018-08-08
  • Genre: Computers
  • Pages : 172
  • ISBN 10 : 9781681733968

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Adversarial Machine Learning 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 f

Artificial Neural Networks and Machine Learning     ICANN 2021 Book

Artificial Neural Networks and Machine Learning ICANN 2021


  • Author : Igor Farkaš
  • Publisher : Springer Nature
  • Release Date : 2021-09-11
  • Genre: Computers
  • Pages : 617
  • ISBN 10 : 9783030863623

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Artificial Neural Networks and Machine Learning ICANN 2021 Excerpt :

The proceedings set LNCS 12891, LNCS 12892, LNCS 12893, LNCS 12894 and LNCS 12895 constitute the proceedings of the 30th International Conference on Artificial Neural Networks, ICANN 2021, held in Bratislava, Slovakia, in September 2021.* The total of 265 full papers presented in these proceedings was carefully reviewed and selected from 496 submissions, and organized in 5 volumes. In this volume, the papers focus on topics such as adversarial machine learning, anomaly detection, attention and transformers, audio and multimodal applications, bioinformatics and biosignal analysis, capsule networks and cognitive models. *The conference was held online 2021 due to the COVID-19 pandemic.

Intelligent Systems and Applications Book

Intelligent Systems and Applications


  • Author : Kohei Arai
  • Publisher : Springer Nature
  • Release Date : 2020-08-25
  • Genre: Technology & Engineering
  • Pages : 794
  • ISBN 10 : 9783030551872

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Intelligent Systems and Applications Excerpt :

The book Intelligent Systems and Applications - Proceedings of the 2020 Intelligent Systems Conference is a remarkable collection of chapters covering a wider range of topics in areas of intelligent systems and artificial intelligence and their applications to the real world. The Conference attracted a total of 545 submissions from many academic pioneering researchers, scientists, industrial engineers, students from all around the world. These submissions underwent a double-blind peer review process. Of those 545 submissions, 177 submissions have been selected to be included in these proceedings. As intelligent systems continue to replace and sometimes outperform human intelligence in decision-making processes, they have enabled a larger number of problems to be tackled more effectively.This branching out of computational intelligence in several directions and use of intelligent systems in everyday applications have created the need for such an international conference which serves as a venue to report on up-to-the-minute innovations and developments. This book collects both theory and application based chapters on all aspects of artificial intelligence, from classical to intelligent scope. We hope that readers find the volume interesting and valuable; it provides the state of the art intelligent methods and techniques for solving real world problems along with a vision of the future research.

Practicing Trustworthy Machine Learning Book

Practicing Trustworthy Machine Learning


  • Author : Yada Pruksachatkun
  • Publisher : "O'Reilly Media, Inc."
  • Release Date : 2023-01-03
  • Genre: Computers
  • Pages : 304
  • ISBN 10 : 9781098120238

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Practicing Trustworthy Machine Learning Excerpt :

With the increasing use of AI in high-stakes domains such as medicine, law, and defense, organizations spend a lot of time and money to make ML models trustworthy. Many books on the subject offer deep dives into theories and concepts. This guide provides a practical starting point to help development teams produce models that are secure, more robust, less biased, and more explainable. Authors Yada Pruksachatkun, Matthew McAteer, and Subhabrata Majumdar translate best practices in the academic literature for curating datasets and building models into a blueprint for building industry-grade trusted ML systems. With this book, engineers and data scientists will gain a much-needed foundation for releasing trustworthy ML applications into a noisy, messy, and often hostile world. You'll learn: Methods to explain ML models and their outputs to stakeholders How to recognize and fix fairness concerns and privacy leaks in an ML pipeline How to develop ML systems that are robust and secure against malicious attacks Important systemic considerations, like how to manage trust debt and which ML obstacles require human intervention

Deep Learning  Algorithms and Applications Book

Deep Learning Algorithms and Applications


  • Author : Witold Pedrycz
  • Publisher : Springer Nature
  • Release Date : 2019-10-23
  • Genre: Technology & Engineering
  • Pages : 360
  • ISBN 10 : 9783030317607

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Deep Learning Algorithms and Applications Excerpt :

This book presents a wealth of deep-learning algorithms and demonstrates their design process. It also highlights the need for a prudent alignment with the essential characteristics of the nature of learning encountered in the practical problems being tackled. Intended for readers interested in acquiring practical knowledge of analysis, design, and deployment of deep learning solutions to real-world problems, it covers a wide range of the paradigm’s algorithms and their applications in diverse areas including imaging, seismic tomography, smart grids, surveillance and security, and health care, among others. Featuring systematic and comprehensive discussions on the development processes, their evaluation, and relevance, the book offers insights into fundamental design strategies for algorithms of deep learning.

Machine Learning and Knowledge Discovery in Databases Book

Machine Learning and Knowledge Discovery in Databases


  • Author : Peggy Cellier
  • Publisher : Springer Nature
  • Release Date : 2020-03-27
  • Genre: Computers
  • Pages : 679
  • ISBN 10 : 9783030438234

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Machine Learning and Knowledge Discovery in Databases Excerpt :

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 Würzburg, 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

Science of Cyber Security


  • Author : Feng Liu
  • Publisher : Springer Nature
  • Release Date : 2019-12-06
  • Genre: Computers
  • Pages : 382
  • ISBN 10 : 9783030346379

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Science of Cyber Security Excerpt :

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

Engineering Dependable and Secure Machine Learning Systems


  • Author : Onn Shehory
  • Publisher : Springer Nature
  • Release Date : 2020-11-07
  • Genre: Computers
  • Pages : 141
  • ISBN 10 : 9783030621445

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Engineering Dependable and Secure Machine Learning Systems Excerpt :

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.

Medical Image Computing and Computer Assisted Intervention     MICCAI 2022 Book

Medical Image Computing and Computer Assisted Intervention MICCAI 2022


  • Author : Linwei Wang
  • Publisher : Springer Nature
  • Release Date : 2022-10-17
  • Genre: Computers
  • Pages : 832
  • ISBN 10 : 9783031164378

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Medical Image Computing and Computer Assisted Intervention MICCAI 2022 Excerpt :

The eight-volume set LNCS 13431, 13432, 13433, 13434, 13435, 13436, 13437, and 13438 constitutes the refereed proceedings of the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, which was held in Singapore in September 2022. The 574 revised full papers presented were carefully reviewed and selected from 1831 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: Brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; heart and lung imaging; dermatology; Part II: Computational (integrative) pathology; computational anatomy and physiology; ophthalmology; fetal imaging; Part III: Breast imaging; colonoscopy; computer aided diagnosis; Part IV: Microscopic image analysis; positron emission tomography; ultrasound imaging; video data analysis; image segmentation I; Part V: Image segmentation II; integration of imaging with non-imaging biomarkers; Part VI: Image registration; image reconstruction; Part VII: Image-Guided interventions and surgery; outcome and disease prediction; surgical data science; surgical planning and simulation; machine learning – domain adaptation and generalization; Part VIII: Machine learning – weakly-supervised learning; machine learning – model interpretation; machine learning – uncertainty; machine learning theory and methodologies.

Computer Vision     ECCV 2022 Book

Computer Vision ECCV 2022


  • Author : Shai Avidan
  • Publisher : Springer Nature
  • Release Date : 2022-11-08
  • Genre: Computers
  • Pages : 817
  • ISBN 10 : 9783031198007

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Computer Vision ECCV 2022 Excerpt :

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

Artificial Neural Networks and Machine Learning     ICANN 2022 Book

Artificial Neural Networks and Machine Learning ICANN 2022


  • Author : Elias Pimenidis
  • Publisher : Springer Nature
  • Release Date : 2022-09-06
  • Genre: Computers
  • Pages : 783
  • ISBN 10 : 9783031159190

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Artificial Neural Networks and Machine Learning ICANN 2022 Excerpt :

The 4-volumes set of LNCS 13529, 13530, 13531, and 13532 constitutes the proceedings of the 31st International Conference on Artificial Neural Networks, ICANN 2022, held in Bristol, UK, in September 2022. The total of 255 full papers presented in these proceedings was carefully reviewed and selected from 561 submissions. ICANN 2022 is a dual-track conference featuring tracks in brain inspired computing and machine learning and artificial neural networks, with strong cross-disciplinary interactions and applications.

Computer Vision     ECCV 2020 Book

Computer Vision ECCV 2020


  • Author : Andrea Vedaldi
  • Publisher : Springer Nature
  • Release Date : 2020-11-02
  • Genre: Computers
  • Pages : 789
  • ISBN 10 : 9783030586041

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Computer Vision ECCV 2020 Excerpt :

The 30-volume set, comprising the LNCS books 12346 until 12375, constitutes the refereed proceedings of the 16th European Conference on Computer Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. The conference was held virtually due to the COVID-19 pandemic. The 1360 revised papers presented in these proceedings were carefully reviewed and selected from a total of 5025 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.

Security  Privacy  and Anonymity in Computation  Communication  and Storage Book

Security Privacy and Anonymity in Computation Communication and Storage


  • Author : Guojun Wang
  • Publisher : Springer Nature
  • Release Date : 2021-02-04
  • Genre: Computers
  • Pages : 422
  • ISBN 10 : 9783030688516

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Security Privacy and Anonymity in Computation Communication and Storage Excerpt :

This book constitutes the refereed proceedings of the 13th International Conference on Security, Privacy, and Anonymity in Computation, Communication, and Storage, SpaCCS 2020, held in Nanjing, China, in December 2020. The 30 full papers were carefully reviewed and selected from 88 submissions. The papers cover many dimensions including security algorithms and architectures, privacy-aware policies, regulations and techniques, anonymous computation and communication, encompassing fundamental theoretical approaches, practical experimental projects, and commercial application systems for computation, communication and storage. SpaCCS 2020 is held jointly with the 11th International Workshop on Trust, Security and Privacy for Big Data (TrustData 2020), the 10th International Symposium on Trust, Security and Privacy for Emerging Applications (TSP 2020), the 9th International Symposium on Security and Privacy on Internet of Things (SPIoT 2020), the 6th International Symposium on Sensor-Cloud Systems (SCS 2020), the 2nd International Workshop on Communication, Computing, Informatics and Security (CCIS 2020), the First International Workshop on Intelligence and Security in Next Generation Networks (ISNGN 2020), the First International Symposium on Emerging Information Security and Applications (EISA 2020).

Machine Learning and Knowledge Discovery in Databases Book

Machine Learning and Knowledge Discovery in Databases


  • Author : Frank Hutter
  • Publisher : Springer Nature
  • Release Date : 2021-02-24
  • Genre: Computers
  • Pages : 742
  • ISBN 10 : 9783030676612

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Machine Learning and Knowledge Discovery in Databases Excerpt :

The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic. The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings. The volumes are organized in topical sections as follows: Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent neural networks; collaborative filtering and matrix completion. Part II: deep learning optimization and theory; active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning. Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data. Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track.