  State-of-the-art deep neural networks (DNNs) have been proved to have
excellent performance on unsupervised domain adaption (UDA). However, recent
work shows that DNNs perform poorly when being attacked by adversarial samples,
where these attacks are implemented by simply adding small disturbances to the
original images. Although plenty of work has focused on this, as far as we
know, there is no systematic research on the robustness of unsupervised domain
adaption model. Hence, we discuss the robustness of unsupervised domain
adaption against adversarial attacking for the first time. We benchmark various
settings of adversarial attack and defense in domain adaption, and propose a
cross domain attack method based on pseudo label. Most importantly, we analyze
the impact of different datasets, models, attack methods and defense methods.
Directly, our work proves the limited robustness of unsupervised domain
adaptation model, and we hope our work may facilitate the community to pay more
attention to improve the robustness of the model against attacking.
