Machine learning is widely used in signal processing which is demonstrated in this book. The success of machine learning in signal processing relies heavily on the quality of the data which is also demonstrated in this book. However, the diverse data sources make it harder to get very high-quality data. What makes it worse is that there might be a malicious adversary, who can deliberately modify the data or add poisoning data to corrupt the learning system. This imposes a significant threat to machine learning in signal processing, for example, in wireless communication, array signal processing, and image signal processing. Hence, it is necessary and urgent to investigate the behavior of machine learning algorithms in signal processing under adversarial attacks. The authors examine the adversarial robustness of three commonly used machine learning algorithms in signal processing: linear regression, LASSO-based feature selection, and principal component analysis (PCA). Based on the theoretical analysis for this book, the authors also carry out adversarial attacks on several signal processing problems, such as feature selection, array signal processing, principal component analysis, wireless sensor networks and more.The first part of this book studies the adversarial robustness of linear regression. The authors assume there is an adversary in the linear regression system and it tries to suppress or promote one of the regression coefficients. To obtain this goal, the adversary adds poisoning data samples or directly modifies the feature matrix of the original data. The authors derive the optimal poisoning data sample and propose an alternating optimization method to design the optimal feature modification. It also demonstrate the effectiveness of the attack against a wireless distributed learning system. The second part of this book extends the linear regression to LASSO-based feature selection and studies the best strategy to modify the feature matrix or response values to mislead the learning system to select the wrong features. The authors formulate this problem as a bi-level optimization problem and use a smooth approximation of the norm function to attain the gradient of our objective function. With the gradient information, the authors employ the projected gradient method to find the optimal attacks. It also illustrates how this attack influences array signal processing and weather data analysis. The last section of this book considers 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 and derive the optimal attack strategy to modify the original data to maximize the subspace distance between the original one and the one after modification. This book also conducts an attack on a principal regression problem and demonstrate its impacts on the subspace and the regression result.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 will also want 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 C

Hersteller:
Springer Verlag GmbH
juergen.hartmann@springer.com
Tiergartenstr. 17
DE 69121 Heidelberg