Machine Learning Algorithms: Adversarial Robustness in Signal Processing
暫譯: 機器學習演算法：信號處理中的對抗穩健性

Name: Machine Learning Algorithms: Adversarial Robustness in Signal Processing
Price: 6080 TWD
Availability: OnlineOnly
Author: Li, Fuwei, Lai, Lifeng, Cui, Shuguang
ISBN: 303116377X

Li, Fuwei, Lai, Lifeng, Cui, Shuguang

出版商: Springer
出版日期: 2023-11-17
售價: $6,400
貴賓價: 9.5 折 $6,080
語言: 英文
頁數: 104
裝訂: Quality Paper - also called trade paper
ISBN: 303116377X
ISBN-13: 9783031163777
相關分類: Machine Learning、Algorithms-data-structures

海外代購書籍(需單獨結帳)

商品描述

This book demonstrates the optimal adversarial attacks against several important signal processing algorithms. Through presenting the optimal attacks in wireless sensor networks, array signal processing, principal component analysis, etc, the authors reveal the robustness of the signal processing algorithms against adversarial attacks. Since data quality is crucial in signal processing, the adversary that can poison the data will be a significant threat to signal processing. Therefore, it is necessary and urgent to investigate the behavior of machine learning algorithms in signal processing under adversarial attacks.

The authors in this book mainly examine the adversarial robustness of three commonly used machine learning algorithms in signal processing respectively: linear regression, LASSO-based feature selection, and principal component analysis (PCA). As to linear regression, the authors derive the optimal poisoning data sample and the optimal feature modifications, and also demonstrate the effectiveness of the attack against a wireless distributed learning system. The authors further extend the linear regression to LASSO-based feature selection and study the best strategy to mislead the learning system to select the wrong features. The authors find the optimal attack strategy by solving a bi-level optimization problem and also illustrate how this attack influences array signal processing and weather data analysis. In the end, the authors consider the adversarial robustness of the subspace learning problem. The authors examine the optimal modification strategy under the energy constraints to delude the PCA-based subspace learning algorithm.

This book targets researchers working in machine learning, electronic information, and information theory as well as advanced-level students studying these subjects. R&D engineers who are working in machine learning, adversarial machine learning, robust machine learning, and technical consultants working on the security and robustness of machine learning are likely to purchase this book as a reference guide.

商品描述(中文翻譯)

這本書展示了針對幾個重要信號處理演算法的最佳對抗攻擊。透過呈現無線感測器網路、陣列信號處理、主成分分析等領域的最佳攻擊，作者揭示了信號處理演算法對對抗攻擊的穩健性。由於數據質量在信號處理中至關重要，能夠污染數據的對手將對信號處理構成重大威脅。因此，調查機器學習演算法在對抗攻擊下的行為是必要且緊迫的。

本書的作者主要分別檢視三種在信號處理中常用的機器學習演算法的對抗穩健性：線性回歸、基於 LASSO 的特徵選擇，以及主成分分析（PCA）。關於線性回歸，作者推導出最佳的污染數據樣本和最佳的特徵修改，並展示了該攻擊對無線分散學習系統的有效性。作者進一步將線性回歸擴展到基於 LASSO 的特徵選擇，並研究誤導學習系統選擇錯誤特徵的最佳策略。作者透過解決一個雙層優化問題來找到最佳攻擊策略，並說明這種攻擊如何影響陣列信號處理和氣象數據分析。最後，作者考慮了子空間學習問題的對抗穩健性，檢視在能量限制下的最佳修改策略，以誤導基於 PCA 的子空間學習演算法。

本書的目標讀者為從事機器學習、電子信息和信息理論的研究人員，以及學習這些科目的高級學生。從事機器學習、對抗機器學習、穩健機器學習的研發工程師，以及專注於機器學習安全性和穩健性的技術顧問，可能會將本書作為參考指南購買。

作者簡介

Fuwei Li received his B.S. and M.S. degrees from University of Electronic Science and Technology of China, Sichuan, China, in 2012 and 2015, respectively. During that time, his research focused on sparse signal processing and Bayesian compressed sensing. He received his Ph.D. degree from University of California, Davis, CA, in 2021. During his Ph.D. study, he mainly focused on the adversarial robustness of machine learning algorithms. Now, he is a scientist of AI perception algorithm at Black Sesame Tech. Inc.Lifeng Lai received the B.E. and M. E. degrees from Zhejiang University, Hangzhou, China in 2001 and 2004 respectively, and the PhD degree from The Ohio State University at Columbus, OH, in 2007. He was a postdoctoral research associate at Princeton University from 2007 to 2009, an assistant professor at University of Arkansas, Little Rock from 2009 to 2012, and an assistant professor at Worcester Polytechnic Institute from 2012 to 2016. He joined the Department of Electrical and Computer Engineering at University of California, Davis as an associate professor in 2016, and was promoted to professor in 2020. His current research interest includes information theory, stochastic signal processing, machine learning and their applications. Dr. Lai was a Distinguished University Fellow of the Ohio State University from 2004 to 2007. He is a co-recipient of the Best Paper Award from IEEE Global Communications Conference (Globecom) in 2008, the Best Paper Award from IEEE Conference on Communications (ICC) in 2011 and the Best Paper Award from IEEE Smart Grid Communications (SmartGridComm) in 2012. He received the National Science Foundation CAREER Award in 2011 and Northrop Young Researcher Award in 2012. He served as a Guest Editor for IEEE Journal on Selected Areas in Communications, Special Issue on Signal Processing Techniques for Wireless Physical Layer Security from 2012 to 2013, an editor for IEEE Transactions on Wireless Communications from 2013 to 2018, and an associate editor for IEEE Transactions on Information Forensics and Security from 2015 to 2020. He is currently serving as an associate editor for IEEE Transactions on Information Theory, IEEE Transactions on Mobile Computing and IEEE Transactions on Signal and Information Processing over Networks.Shuguang Cui received his Ph.D in Electrical Engineering from Stanford University, California, USA, in 2005. Afterwards, he has been working as assistant, associate, full, Chair Professor in Electrical and Computer Engineering at the Univ. of Arizona, Texas A&M University, UC Davis, and CUHK at Shenzhen respectively. He has also served as the Executive Dean for the School of Science and Engineering at CUHK, Shenzhen, the Director for the Future Network of Intelligence Institute, and the Executive Vice Director at Shenzhen Research Institute of Big Data. His current research interests focus on data driven large-scale system control and resource management, large data set analysis, IoT system design, energy harvesting based communication system design, and cognitive network optimization. He was selected as the Thomson Reuters Highly Cited Researcher and listed in the Worlds' Most Influential Scientific Minds by ScienceWatch in 2014. He was the recipient of the IEEE Signal Processing Society 2012 Best Paper Award. He has served as the general co-chair and TPC co-chairs for many IEEE conferences. He has also been serving as the area editor for IEEE Signal Processing Magazine, and associate editors for IEEE Transactions on Big Data, IEEE Transactions on Signal Processing, IEEE JSAC Series on Green Communications and Networking, and IEEE Transactions on Wireless Communications. He has been the elected member for IEEE Signal Processing Society SPCOM Technical Committee (2009 2014) and the elected Chair for IEEE ComSoc Wireless Technical Committee (2017 2018). He is a member of the Steering Committee for IEEE Transactions on Big Data and the Chair of the Steering Committee for IEEE Transactions on Cognitive Communications and Networking. He was also a member of the IEEE ComSoc Emerging Technology Committee. He was elected as an IEEE Fellow in 2013, an IEEE ComSoc Distinguished Lecturer in 2014, and IEEE VT Society Distinguished Lecturer in 2019. He has won the IEEE ICC best paper award, ICIP best paper finalist, and the IEEE Globecom best paper award all in 2020.

作者簡介(中文翻譯)

李福偉於2012年和2015年分別獲得中國四川電子科技大學的學士和碩士學位。在此期間，他的研究重點是稀疏信號處理和貝葉斯壓縮感知。他於2021年獲得加州大學戴維斯分校的博士學位。在博士研究期間，他主要專注於機器學習算法的對抗穩健性。目前，他是黑芝麻科技公司的人工智慧感知算法科學家。賴立峰於2001年和2004年分別獲得中國浙江大學的工程學學士和碩士學位，並於2007年獲得俄亥俄州立大學的博士學位。他於2007年至2009年在普林斯頓大學擔任博士後研究助理，2009年至2012年在阿肯色大學小石城擔任助理教授，2012年至2016年在伍斯特理工學院擔任助理教授。他於2016年加入加州大學戴維斯分校電機與計算機工程系，擔任副教授，並於2020年晉升為教授。他目前的研究興趣包括信息理論、隨機信號處理、機器學習及其應用。賴博士於2004年至2007年期間是俄亥俄州立大學的傑出大學研究員。他是2008年IEEE全球通信會議（Globecom）最佳論文獎、2011年IEEE通信會議（ICC）最佳論文獎及2012年IEEE智慧電網通信（SmartGridComm）最佳論文獎的共同獲得者。他於2011年獲得國家科學基金會的CAREER獎，並於2012年獲得諾斯羅普青年研究者獎。他曾於2012年至2013年擔任IEEE選定通信區域期刊的特刊客座編輯，2013年至2018年擔任IEEE無線通信期刊的編輯，2015年至2020年擔任IEEE信息取證與安全期刊的副編輯。目前，他擔任IEEE信息理論期刊、IEEE移動計算期刊及IEEE網絡信號與信息處理期刊的副編輯。崔曙光於2005年在美國加州斯坦福大學獲得電機工程博士學位。此後，他在亞利桑那大學、德克薩斯農工大學、加州大學戴維斯分校及深圳中文大學的電機與計算機工程系擔任助理教授、副教授、正教授及講座教授。他還曾擔任深圳中文大學科學與工程學院的執行院長、未來智能網絡研究所所長及深圳大數據研究院的執行副所長。他目前的研究興趣集中在數據驅動的大規模系統控制和資源管理、大數據集分析、物聯網系統設計、基於能量收集的通信系統設計及認知網絡優化。他於2014年被選為Thomson Reuters高被引研究者，並被ScienceWatch列入全球最具影響力的科學思想家名單。他是2012年IEEE信號處理學會最佳論文獎的獲得者。他曾擔任多個IEEE會議的總共同主席和技術程序委員會共同主席。他還擔任IEEE信號處理雜誌的區域編輯，以及IEEE大數據期刊、IEEE信號處理期刊、IEEE綠色通信與網絡系列期刊及IEEE無線通信期刊的副編輯。他曾是IEEE信號處理學會SPCOM技術委員會的當選成員（2009-2014）及IEEE通信學會無線技術委員會的當選主席（2017-2018）。他是IEEE大數據期刊的指導委員會成員，並擔任IEEE認知通信與網絡期刊的指導委員會主席。他還曾是IEEE通信學會新興技術委員會的成員。他於2013年當選為IEEE Fellow，2014年成為IEEE通信學會的傑出講者，2019年成為IEEE VT學會的傑出講者。他在2020年獲得IEEE ICC最佳論文獎、ICIP最佳論文決賽入圍獎及IEEE Globecom最佳論文獎。