Title: Query-limited Black-box Attacks to Classifiers,
Abstract: We study black-box attacks on machine learning classifiers where each query
to the model incurs some cost or risk of detection to the adversary. We focus
explicitly on minimizing the number of queries as a major objective.
Specifically, we consider the problem of attacking machine learning classifiers
subject to a budget of feature modification cost while minimizing the number of
queries, where each query returns only a class and confidence score. We
describe an approach that uses Bayesian optimization to minimize the number of
queries, and find that the number of queries can be reduced to approximately
one tenth of the number needed through a random strategy for scenarios where
the feature modification cost budget is low.