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.
