论文标题
遥感中的通用对手示例：方法和基准测试
Universal Adversarial Examples in Remote Sensing: Methodology and Benchmark
论文作者
论文摘要
深度神经网络在许多重要的遥感任务中取得了巨大的成功。然而，不应忽略它们对对抗性例子的脆弱性。在这项研究中，我们第一次系统地在遥感数据中系统地分析了普遍的对抗示例，而没有受害者模型的任何知识。具体而言，我们提出了一种新型的黑盒对抗攻击方法，即混合攻击及其简单的变体混合尺寸攻击，用于遥感数据。提出的方法的关键思想是通过攻击给定替代模型的浅层层中的特征来找到不同网络之间的共同漏洞。尽管它们很简单，但提出的方法仍可以生成可转移的对抗性示例，这些示例欺骗了场景分类和语义分割任务中最先进的深层神经网络，并具有很高的成功率。我们进一步在名为UAE-RS的数据集中提供了生成的通用对抗示例，这是第一个在遥感字段中提供黑色框对面样本的数据集。我们希望阿联酋可以用作基准，以帮助研究人员设计具有对遥感领域对抗性攻击的强烈抵抗力的深层神经网络。代码和阿联酋-RS数据集可在线获得（https://github.com/yonghaoxu/uae-rs）。
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).
