Classifiers are often used to detect miscreant activities. We study how an
adversary can efficiently query a classifier to elicit information that allows
the adversary to evade detection at near-minimal cost. We generalize results of
Lowd and Meek (2005) to convex-inducing classifiers. We present algorithms that
construct undetected instances of near-minimal cost using only polynomially
many queries in the dimension of the space and without reverse engineering the
decision boundary.Comment: 8 pages; to appear at AISTATS'201