  Extensive Unsupervised Domain Adaptation (UDA) studies have shown great
success in practice by learning transferable representations across a labeled
source domain and an unlabeled target domain with deep models. However,
previous works focus on improving the generalization ability of UDA models on
clean examples without considering the adversarial robustness, which is crucial
in real-world applications. Conventional adversarial training methods are not
suitable for the adversarial robustness on the unlabeled target domain of UDA
since they train models with adversarial examples generated by the supervised
loss function. In this work, we leverage intermediate representations learned
by multiple robust ImageNet models to improve the robustness of UDA models. Our
method works by aligning the features of the UDA model with the robust features
learned by ImageNet pre-trained models along with domain adaptation training.
It utilizes both labeled and unlabeled domains and instills robustness without
any adversarial intervention or label requirement during domain adaptation
training. Experimental results show that our method significantly improves
adversarial robustness compared to the baseline while keeping clean accuracy on
various UDA benchmarks.
