Machine learning in adversarial environments

A adversarial environment classifier training method, comprising:

• identifying a number of features associated with each sample included in an initial data set that includes a plurality of samples;
• allocating at least a portion of the samples included in the initial data set into at least a training data set;
• identifying at least one set of compromiseable features for at least a portion of the samples included in the training data set;
• defining a classifier loss function [l(xi, yi, w)] that includes:
• a feature vector (xi) for each sample included in the training data set;
• a label (yi) for each sample included in the training data set;
• and
• a weight vector (w) associated with the classifier;
• and
• determining the minmax of the classifier loss function (minwmaxi l(xi, yi, w)).

A storage device that includes machine-readable instructions that, when executed, physically transform a configurable circuit to an adversarial machine-learning training circuit, the adversarial environment classifier training circuit to:

• identify a number of features associated with each sample included in a training data set that includes a plurality of samples;
• allocate at least a portion of the samples included in the initial data set to at least a training data set;
• identify at least one set of compromiseable features for at least a portion of the samples included in the training data set;
• define a classifier loss function [l(xi, yi, w)] that includes:
• a feature vector (xi) for each sample included in the training data set;
• a label (yi) for each sample included in the training data set;
• and
• a weight vector (w) associated with the classifier;
• and
• determine the minmax of the classifier loss function (minwmaxi l(xi, yi, w)).

A classifier training system, comprising:

• a means for identifying a number of features associated with each sample included in an initial data set that includes a plurality of samples;
• a means for allocating at least a portion of the samples included in the initial data set into at least a training data set;
• a means for identifying at least one set of compromiseable features for at least a portion of the samples included in the training data set;
• a means for defining a classifier loss function [l(xi, yi, w)] that includes:
• a feature vector (xi) for each sample included in the training data set;
• a label (yi) for each sample included in the training data set;
• and
• a weight vector (w) associated with the classifier;
• and
• a means for determining the minmax of the classifier loss function (minwmaxi l(xi, yi, w)).