Classifiers are often used to detect miscreant activities. We study how an adversary can systematically query a classifier to elicit information that allows the adversary to evade detection while incurring a near-minimal cost of modifying their intended malfeasance. We generalize the theory of Lowd and Meek (2005) to the family of convex-inducing classifiers that partition input space into two sets one of which is convex. We present query algorithms for this family that construct undetected instances of approximately minimal cost using only polynomially-many queries in the dimension of the space and in the level of approximation. Our results demonstrate that near-optimal evasion can be accomplished without reverseengineering the classifier's decision boundary. We also consider general ℓ p costs and show that near-optimal evasion on the family of convex-inducing classifiers is generally efficient for both positive and negative convexity for all levels of approximation if p = 1.
However, they are attacked by adversaries who generate exploratory queries to elicit information that in return allows them to evade detection REF .
Query Strategies for Evading Convex-Inducing Classifiers
ArXiv
ArXiv
Mathematics Computer Science