Machine Learning Algorithms: Adversarial Robustness in Signal Processing

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.

​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.

​Fuwei Li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，他的研究主要集中在稀疏信號處理和貝葉斯壓縮感知上。他於2021年獲得加利福尼亞大學戴維斯分校的博士學位。在博士研究期間，他主要關注機器學習算法的對抗魯棒性。現在，他是黑芝麻科技公司的AI感知算法科學家。Lifeng Lai於2001年和2004年分別獲得中國杭州浙江大學的學士和碩士學位，並於2007年獲得美國俄亥俄州立大學的博士學位。他曾在2007年至2009年期間擔任普林斯頓大學的博士後研究員，2009年至2012年期間擔任阿肯色大學小岩城分校的助理教授，2012年至2016年期間擔任伍斯特理工學院的助理教授。他於2016年加入加利福尼亞大學戴維斯分校的電氣和計算機工程系，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。Lai博士曾是俄亥俄州立大學的傑出大學研究員（2004-2007年）。他是2008年IEEE全球通信大會（Globecom）最佳論文獎、2011年IEEE通信大會（ICC）最佳論文獎和2012年IEEE智能電網通信（SmartGridComm）最佳論文獎的共同獲獎者。他於2011年獲得國家科學基金會職業生涯獎和2012年Northrop青年研究員獎。他曾擔任2012年至2013年IEEE選定領域通信期刊的客座編輯，專題為無線物理層安全的信號處理技術，2013年至2018年IEEE無線通信交易的編輯，2015年至2020年IEEE信息取證和安全交易的副編輯。他目前擔任IEEE信息理論交易、IEEE移動計算交易和IEEE信號與信息處理交易的副編輯。Shuguang Cui於2005年獲得美國斯坦福大學的電氣工程博士學位。之後，他先後在亞利桑那大學、德克薩斯農工大學、加利福尼亞大學戴維斯分校和中國香港中文大學深圳研究院擔任助理教授、副教授、教授和主席教授。他還曾擔任中國香港中文大學深圳研究院科學與工程學院的執行院長，未來智能網絡研究所的所長，以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、能量收集通信系統設計和認知網絡優化。他於2014年被選為湯森路透高被引研究員，並被ScienceWatch列為世界上最有影響力的科學家之一。他是IEEE信號處理學會2012年最佳論文獎的獲得者。他曾擔任多個IEEE會議的總聯合主席和技術計劃委員會聯合主席。他還擔任IEEE信號處理雜誌的領域編輯，以及IEEE大數據交易、IEEE信號處理交易、IEEE JSAC系列綠色通信和網絡交易，以及IEEE無線通信交易的副編輯。他曾擔任IEEE信號處理學會SPCOM技術委員會（2009-2014年）的當選成員，以及IEEE通信學會無線技術委員會（2017-2018年）的當選主席。他是IEEE大數據交易的指導委員會成員，以及IEEE Transactions on Big Data和Cha的指導委員會成員。