  Self-supervised learning (SSL) methods have resulted in broad improvements to
neural network performance by leveraging large, untapped collections of
unlabeled data to learn generalized underlying structure. In this work, we
harness unsupervised data augmentation (UDA), an SSL technique, to mitigate
backdoor or Trojan attacks on deep neural networks. We show that UDA is more
effective at removing trojans than current state-of-the-art methods for both
feature space and point triggers, over a range of model architectures, trojans,
and data quantities provided for trojan removal. These results demonstrate that
UDA is both an effective and practical approach to mitigating the effects of
backdoors on neural networks.
