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In this paper, we consider batch supervised learning where an adversary isallowed to corrupt instances with arbitrarily large noise. The adversary isallowed to corrupt any $l$ features in each instance and the adversary canchange their values in any way. This noise is introduced on test instances andthe algorithm receives no label feedback for these instances. We provideseveral subspace voting techniques that can be used to transform existingalgorithms and prove data-dependent performance bounds in this setting. The keyinsight to our results is that we set our parameters so that a significantfraction of the voting hypotheses do not contain corrupt features and, for manyreal world problems, these uncorrupt hypotheses are sufficient to achieve highaccuracy. We empirically validate our approach on several datasets includingthree new datasets that deal with side channel electromagnetic information.

Subspace Methods That Are Resistant to a Limited Number of Features Corrupted by an Adversary