SanitAIs: Unsupervised Data Augmentation to Sanitize Trojaned Neural Networks
This addresses security vulnerabilities in neural networks for applications requiring robust AI systems, representing a strong specific gain in defense mechanisms.
The paper tackles the problem of backdoor or Trojan attacks on deep neural networks by using unsupervised data augmentation (UDA), showing it is more effective than current state-of-the-art methods at removing trojans across various triggers and architectures.
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.