TITLE: Poisoning the Unlabeled Dataset of Semi-Supervised Learning 

ABSTRACT: Semi-supervised machine learning models learn from a (small) set of labeled
training examples, and a (large) set of unlabeled training examples.
State-of-the-art models can reach within a few percentage points of
fully-supervised training, while requiring 100x less labeled data.
  We study a new class of vulnerabilities: poisoning attacks that modify the
unlabeled dataset. In order to be useful, unlabeled datasets are given strictly
less review than labeled datasets, and adversaries can therefore poison them
easily. By inserting maliciously-crafted unlabeled examples totaling just 0.1%
of the dataset size, we can manipulate a model trained on this poisoned dataset
to misclassify arbitrary examples at test time (as any desired label). Our
attacks are highly effective across datasets and semi-supervised learning
methods.
  We find that more accurate methods (thus more likely to be used) are
significantly more vulnerable to poisoning attacks, and as such better training
methods are unlikely to prevent this attack. To counter this we explore the
space of defenses, and propose two methods that mitigate our attack.
Semi-supervised machine learning models learn from a (small) set of labeled
training examples, and a (large) set of unlabeled training examples.
State-of-the-art models can reach within a few percentage points of
fully-supervised training, while requiring 100x less labeled data.
  We study a new class of vulnerabilities: poisoning attacks that modify the
unlabeled dataset. In order to be useful, unlabeled datasets are given strictly
less review than labeled datasets, and adversaries can therefore poison them
easily. By inserting maliciously-crafted unlabeled examples totaling just 0.1%
of the dataset size, we can manipulate a model trained on this poisoned dataset
to misclassify arbitrary examples at test time (as any desired label). Our
attacks are highly effective across datasets and semi-supervised learning
methods.
  We find that more accurate methods (thus more likely to be used) are
significantly more vulnerable to poisoning attacks, and as such better training
methods are unlikely to prevent this attack. To counter this we explore the
space of defenses, and propose two methods that mitigate our attack.
Poisoning the Unlabeled Dataset of Semi-Supervised Learning
http://arxiv.org/abs/2105.01622v2