document: Adversarial learning Many classification tasks , such as spam filtering , intrusion detection , and terrorism detection , are complicated by an adversary who wishes to avoid detection . Previous work on adversarial classification has made the unrealistic assumption that the attacker has perfect knowledge of the classifier ( 2 ) . In this paper , we introduce the adversarial classifier reverse engineering ( ACRE ) learning problem , the task of learning sufficient information about a classifier to construct adversarial attacks . We present efficient algorithms for reverse engineering linear classifiers with either continuous or Boolean features and demonstrate their effectiveness using real data from the domain of spam filtering .
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