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%.
翻译:最近,随着遥感图像领域深层学习的应用,RSI的分类准确性与传统技术相比大大提高了,然而,即使是最先进的物体识别神经神经网络也被全球对抗性扰动(UAP)所愚弄。关于UAP的研究大多限于普通图像,而RSI尚未研究。为了探索登记册系统综合评估方案的基本特征,本文件提出了一种新颖的方法,将编码器-解码器网络与关注生成RSI统一评估方案的机制结合起来。首先,前者用于生成UAP,可以更好地了解扰动分布,然后后者用于查找RSI分类模型所涉敏感区域。最后,产生的区域被用来微调使模型分类错误的扰动,减少扰动。实验结果表明,UAP可以使分类模型分类错误化,而我们在RSI数据集的拟议方法攻击成功率高达97.09 %。