  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 an {\it a priori} unknown subset of the
training set, and with no clean validation set available; 2) applies a mixture
model both to well-fit potentially multi-modal class distributions and to
capture poisoned samples within a small subset of the mixture components; 3)
jointly identifies poisoned components and samples by minimizing the BIC cost
defined over the whole training set, with the identified poisoned data removed
prior to classifier training. Our experimental results, for various classifier
structures and benchmark datasets, demonstrate the effectiveness and
universality of our defense under strong DP attacks, as well as its superiority
over other works.
