Übersicht			Auf mobile öffnen

Lieferstatus:	i.d.R. innert 7-14 Tagen versandfertig
Veröffentlichung:	November 2022
Genre:	EDV / Informatik
	AdversarialAttack / adversarialmachinelearning / adversarialrobustness / alternatingoptimization / Bi-levelOptimization / interpretablemachinelearning / Lasso / linearregression / machinelearning / Maschinelles Lernen / non-convexoptimization / principalcomponentanalysis / security-criticalmachinelearning / signalprocessing / SubspaceLearning mehr...
ISBN:	9783031163746
EAN-Code:	9783031163746
Verlag:	Springer
Einband:	Gebunden
Sprache:	English
Dimensionen:	H 241 mm / B 160 mm / D 13 mm
Gewicht:	348 gr
Seiten:	116
Bewertung:	Titel bewerten / Meinung schreiben

Inhalt:

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.