MACHINE LEARNING IN ADVERSARIAL ENVIRONMENTS

摘要

An adversarial environment classifier training system includes feature extraction circuitry to identify a number of features associated with each sample included in an initial data set that includes a plurality of samples. The system further includes sample allocation circuitry to allocate at least a portion of the samples included in the initial data set to at least a training data set; machine-learning circuitry communicably coupled to the sample allocation circuitry, the machine-learning circuitry to: identify at least one set of compromiseable features for at least a portion of the initial data set; define a classifier loss function [l(x_i, y_i, w)] that includes: a feature vector (x_i) for each sample included in the initial data set; a label (y_i) for each sample included in the initial data set; and a weight vector (w) associated with the classifier; and determine the minmax of the classifier loss function (min_wmax_i l(x_i, y_i, w)).