  We resolve one of the major outstanding problems in robust statistics. In
particular, if $X$ is an evenly weighted mixture of two arbitrary
$d$-dimensional Gaussians, we devise a polynomial time algorithm that given
access to samples from $X$ an $\eps$-fraction of which have been adversarially
corrupted, learns $X$ to error $\poly(\eps)$ in total variation distance.
