  Deep neural networks have achieved great success in many important remote
sensing tasks. Nevertheless, their vulnerability to adversarial examples should
not be neglected. In this study, we systematically analyze the universal
adversarial examples in remote sensing data for the first time, without any
knowledge from the victim model. Specifically, we propose a novel black-box
adversarial attack method, namely Mixup-Attack, and its simple variant
Mixcut-Attack, for remote sensing data. The key idea of the proposed methods is
to find common vulnerabilities among different networks by attacking the
features in the shallow layer of a given surrogate model. Despite their
simplicity, the proposed methods can generate transferable adversarial examples
that deceive most of the state-of-the-art deep neural networks in both scene
classification and semantic segmentation tasks with high success rates. We
further provide the generated universal adversarial examples in the dataset
named UAE-RS, which is the first dataset that provides black-box adversarial
samples in the remote sensing field. We hope UAE-RS may serve as a benchmark
that helps researchers to design deep neural networks with strong resistance
toward adversarial attacks in the remote sensing field. Codes and the UAE-RS
dataset are available online (https://github.com/YonghaoXu/UAE-RS).
