  Recently, with the application of deep learning in the remote sensing image
(RSI) field, the classification accuracy of the RSI has been dramatically
improved compared with traditional technology. However, even the
state-of-the-art object recognition convolutional neural networks are fooled by
the universal adversarial perturbation (UAP). The research on UAP is mostly
limited to ordinary images, and RSIs have not been studied. To explore the
basic characteristics of UAPs of RSIs, this paper proposes a novel method
combining an encoder-decoder network with an attention mechanism to generate
the UAP of RSIs. Firstly, the former is used to generate the UAP, which can
learn the distribution of perturbations better, and then the latter is used to
find the sensitive regions concerned by the RSI classification model. Finally,
the generated regions are used to fine-tune the perturbation making the model
misclassified with fewer perturbations. The experimental results show that the
UAP can make the classification model misclassify, and the attack success rate
of our proposed method on the RSI data set is as high as 97.09%.
