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Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies

Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies
  • Author : National Academies of Sciences, Engineering, and Medicine
  • Publsiher : National Academies Press
  • Release : 22 August 2019
  • ISBN : 0309496098
  • Pages : 82 pages
  • Rating : 4/5 from 21 ratings
GET THIS BOOKRobust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies

Summary:
The Intelligence Community Studies Board (ICSB) of the National Academies of Sciences, Engineering, and Medicine convened a workshop on December 11â€"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.


Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies

Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies
  • Author : National Academies of Sciences, Engineering, and Medicine,Division on Engineering and Physical Sciences,Computer Science and Telecommunications Board,Board on Mathematical Sciences and Analytics,Intelligence Community Studies Board
  • Publisher : National Academies Press
  • Release : 22 August 2019
GET THIS BOOKRobust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies

The Intelligence Community Studies Board (ICSB) of the National Academies of Sciences, Engineering, and Medicine convened a workshop on December 11â€"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.

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Adversarial Robustness of Deep Learning Models

Adversarial Robustness of Deep Learning Models
  • Author : Samarth Gupta (S.M.)
  • Publisher : Anonim
  • Release : 16 May 2022
GET THIS BOOKAdversarial Robustness of Deep Learning Models

Efficient operation and control of modern day urban systems such as transportation networks is now more important than ever due to huge societal benefits. Low cost network-wide sensors generate large amounts of data which needs to processed to extract useful information necessary for operational maintenance and to perform real-time control. Modern Machine Learning (ML) systems, particularly Deep Neural Networks (DNNs), provide a scalable solution to the problem of information retrieval from sensor data. Therefore, Deep Learning systems are increasingly playing

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Artificial Neural Networks and Machine Learning – ICANN 2021

Artificial Neural Networks and Machine Learning – ICANN 2021
  • Author : Igor Farkaš,Paolo Masulli,Sebastian Otte,Stefan Wermter
  • Publisher : Springer Nature
  • Release : 11 September 2021
GET THIS BOOKArtificial Neural Networks and Machine Learning – ICANN 2021

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

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Deep Learning: Algorithms and Applications

Deep Learning: Algorithms and Applications
  • Author : Witold Pedrycz,Shyi-Ming Chen
  • Publisher : Springer Nature
  • Release : 23 October 2019
GET THIS BOOKDeep Learning: Algorithms and Applications

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,

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Adversarial Robustness for Machine Learning Models

Adversarial Robustness for Machine Learning Models
  • Author : Pin-Yu Chen,Cho-Jui Hsieh
  • Publisher : Academic Press
  • Release : 15 September 2022
GET THIS BOOKAdversarial Robustness for Machine Learning Models

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. Adversarial robustness has become one of the mainstream topics in machine learning with much research carried out, while many companies have started to incorporate security and robustness into their systems. Adversarial Robustness for Machine Learning

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Evaluating and Understanding Adversarial Robustness in Deep Learning

Evaluating and Understanding Adversarial Robustness in Deep Learning
  • Author : Jinghui Chen
  • Publisher : Anonim
  • Release : 16 May 2022
GET THIS BOOKEvaluating and Understanding Adversarial Robustness in Deep Learning

Deep Neural Networks (DNNs) have made many breakthroughs in different areas of artificial intelligence. However, recent studies show that DNNs are vulnerable to adversarial examples. A tiny perturbation on an image that is almost invisible to human eyes could mislead a well-trained image classifier towards misclassification. This raises serious security concerns and trustworthy issues towards the robustness of Deep Neural Networks in solving real world challenges. Researchers have been working on this problem for a while and it has further

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Intelligent Systems and Applications

Intelligent Systems and Applications
  • Author : Kohei Arai,Supriya Kapoor,Rahul Bhatia
  • Publisher : Springer Nature
  • Release : 25 August 2020
GET THIS BOOKIntelligent Systems and Applications

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

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Adversarial Machine Learning

Adversarial Machine Learning
  • Author : Yevgeniy Vorobeychik,Murat Kantarcioglu
  • Publisher : Morgan & Claypool Publishers
  • Release : 08 August 2018
GET THIS BOOKAdversarial Machine Learning

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

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Perturbations, Optimization, and Statistics

Perturbations, Optimization, and Statistics
  • Author : Tamir Hazan,George Papandreou,Daniel Tarlow
  • Publisher : MIT Press
  • Release : 23 December 2016
GET THIS BOOKPerturbations, Optimization, and Statistics

A description of perturbation-based methods developed in machine learning to augment novel optimization methods with strong statistical guarantees. In nearly all machine learning, decisions must be made given current knowledge. Surprisingly, making what is believed to be the best decision is not always the best strategy, even when learning in a supervised learning setting. An emerging body of work on learning under different rules applies perturbations to decision and learning procedures. These methods provide simple and highly efficient learning rules

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Advances in Reliably Evaluating and Improving Adversarial Robustness

Advances in Reliably Evaluating and Improving Adversarial Robustness
  • Author : Jonas Rauber
  • Publisher : Anonim
  • Release : 16 May 2022
GET THIS BOOKAdvances in Reliably Evaluating and Improving Adversarial Robustness

Machine learning has made enormous progress in the last five to ten years. We can now make a computer, a machine, learn complex perceptual tasks from data rather than explicitly programming it. When we compare modern speech or image recognition systems to those from a decade ago, the advances are awe-inspiring. The susceptibility of machine learning systems to small, maliciously crafted adversarial perturbations is less impressive. Almost imperceptible pixel shifts or background noises can completely derail their performance. While humans

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Strengthening Deep Neural Networks

Strengthening Deep Neural Networks
  • Author : Katy Warr
  • Publisher : "O'Reilly Media, Inc."
  • Release : 03 July 2019
GET THIS BOOKStrengthening Deep Neural Networks

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

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Intelligent Technologies and Applications

Intelligent Technologies and Applications
  • Author : Sule Yildirim Yayilgan,Imran Sarwar Bajwa,Filippo Sanfilippo
  • Publisher : Springer Nature
  • Release : 14 March 2021
GET THIS BOOKIntelligent Technologies and Applications

This book constitutes the refereed post-conference proceedings of the Third International Conference on Intelligent Technologies and Applications, INTAP 2020, held in Grimstad, Norway, in September 2020. The 30 revised full papers and 4 revised short papers presented were carefully reviewed and selected from 117 submissions. The papers of this volume are organized in topical sections on image, video processing and analysis; security and IoT; health and AI; deep learning; biometrics; intelligent environments; intrusion and malware detection; and AIRLEAs.

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Artificial Neural Networks and Machine Learning – ICANN 2021

Artificial Neural Networks and Machine Learning – ICANN 2021
  • Author : Igor Farkaš,Paolo Masulli,Sebastian Otte,Stefan Wermter
  • Publisher : Springer Nature
  • Release : 10 September 2021
GET THIS BOOKArtificial Neural Networks and Machine Learning – ICANN 2021

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 computer vision and object detection, convolutional neural networks and kernel methods, deep learning and optimization, distributed and continual learning, explainable methods, few-shot

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