  We consider the problem of prediction by a machine learning algorithm, called
learner, within an adversarial learning setting. The learner's task is to
correctly predict the class of data passed to it as a query. However, along
with queries containing clean data, the learner could also receive malicious or
adversarial queries from an adversary. The objective of the adversary is to
evade the learner's prediction mechanism by sending adversarial queries that
result in erroneous class prediction by the learner, while the learner's
objective is to reduce the incorrect prediction of these adversarial queries
without degrading the prediction quality of clean queries. We propose a game
theory-based technique called a Repeated Bayesian Sequential Game where the
learner interacts repeatedly with a model of the adversary using self play to
determine the distribution of adversarial versus clean queries. It then
strategically selects a classifier from a set of pre-trained classifiers that
balances the likelihood of correct prediction for the query along with reducing
the costs to use the classifier. We have evaluated our proposed technique using
clean and adversarial text data with deep neural network-based classifiers and
shown that the learner can select an appropriate classifier that is
commensurate with the query type (clean or adversarial) while remaining aware
of the cost to use the classifier.
