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A BIC-based Mixture Model Defense against Data Poisoning Attacks on Classifiers. (arXiv:2105.13530v2 [cs.LG] UPDATED)
May 13, 2022, 1:20 a.m. | Xi Li, David J. Miller, Zhen Xiang, George Kesidis
cs.CR updates on arXiv.org arxiv.org
Data Poisoning (DP) is an effective attack that causes trained classifiers to
misclassify their inputs. DP attacks significantly degrade a classifier's
accuracy by covertly injecting attack samples into the training set. Broadly
applicable to different classifier structures, without strong assumptions about
the attacker, an {\it unsupervised} Bayesian Information Criterion (BIC)-based
mixture model defense against "error generic" DP attacks is herein proposed
that: 1) addresses the most challenging {\it embedded} DP scenario wherein, if
DP is present, the poisoned samples are …
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