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We study black-box attacks on machine learning classifiers where each queryto the model incurs some cost or risk of detection to the adversary. We focusexplicitly on minimizing the number of queries as a major objective.Specifically, we consider the problem of attacking machine learning classifierssubject to a budget of feature modification cost while minimizing the number ofqueries, where each query returns only a class and confidence score. Wedescribe an approach that uses Bayesian optimization to minimize the number ofqueries, and find that the number of queries can be reduced to approximatelyone tenth of the number needed through a random strategy for scenarios wherethe feature modification cost budget is low.

Query-limited Black-box Attacks to Classifiers