  Semi-supervised learning methods can train high-accuracy machine learning
models with a fraction of the labeled training samples required for traditional
supervised learning. Such methods do not typically involve close review of the
unlabeled training samples, making them tempting targets for data poisoning
attacks. In this paper we investigate the vulnerabilities of semi-supervised
learning methods to backdoor data poisoning attacks on the unlabeled samples.
We show that simple poisoning attacks that influence the distribution of the
poisoned samples' predicted labels are highly effective - achieving an average
attack success rate as high as 96.9%. We introduce a generalized attack
framework targeting semi-supervised learning methods to better understand and
exploit their limitations and to motivate future defense strategies.
