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- W3199933797 abstract "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." @default.
- W3199933797 created "2021-09-27" @default.
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- W3199933797 date "2010-03-13" @default.
- W3199933797 modified "2023-10-18" @default.
- W3199933797 title "Near-Optimal Evasion of Convex-Inducing Classifiers" @default.
- W3199933797 doi "https://doi.org/10.48550/arxiv.1003.2751" @default.
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