  Unsupervised domain adaptation (UDA) is widely used to transfer knowledge
from a labeled source domain to an unlabeled target domain with different data
distribution. While extensive studies attested that deep learning models are
vulnerable to adversarial attacks, the adversarial robustness of models in
domain adaptation application has largely been overlooked. This paper points
out that the inevitable domain distribution deviation in UDA is a critical
barrier to model robustness on the target domain. To address the problem, we
propose a novel Class-consistent Unsupervised Robust Domain Adaptation (CURDA)
framework for training robust UDA models. With the introduced contrastive
robust training and source anchored adversarial contrastive losses, our
proposed CURDA framework can effectively robustify UDA models by simultaneously
minimizing the data distribution deviation and the distance between target
domain clean-adversarial pairs without creating classification confusion.
Experiments on several public benchmarks show that CURDA can significantly
improve model robustness in the target domain with only minor cost of accuracy
on the clean samples.
