Detecting the salient objects in a remote sensing image has wide applications for the interdisciplinary research. Many existing deep learning methods have been proposed for Salient Object Detection (SOD) in remote sensing images and get remarkable results. However, the recent adversarial attack examples, generated by changing a few pixel values on the original remote sensing image, could result in a collapse for the well-trained deep learning based SOD model. Different with existing methods adding perturbation to original images, we propose to jointly tune adversarial exposure and additive perturbation for attack and constrain image close to cloudy image as Adversarial Cloud. Cloud is natural and common in remote sensing images, however, camouflaging cloud based adversarial attack and defense for remote sensing images are not well studied before. Furthermore, we design DefenseNet as a learn-able pre-processing to the adversarial cloudy images so as to preserve the performance of the deep learning based remote sensing SOD model, without tuning the already deployed deep SOD model. By considering both regular and generalized adversarial examples, the proposed DefenseNet can defend the proposed Adversarial Cloud in white-box setting and other attack methods in black-box setting. Experimental results on a synthesized benchmark from the public remote sensing SOD dataset (EORSSD) show the promising defense against adversarial cloud attacks.

翻译：检测遥感图像中的显著性目标在交叉学科研究中具有广泛应用。现有许多深度学习方法已被提出用于遥感图像显著性目标检测（Salient Object Detection, SOD），并取得了显著成果。然而，近期通过更改原始遥感图像中少量像素值生成的对抗性攻击示例，可能导致基于深度学习的SOD模型性能崩溃。与现有方法在原始图像上添加扰动不同，我们提出联合调整对抗性曝光度和加性扰动进行攻击，并将图像约束至接近多云图像，即对抗性云（Adversarial Cloud）。云在遥感图像中自然且常见，然而针对遥感图像的伪装云对抗攻击与防御此前尚未得到充分研究。此外，我们设计了DefenseNet作为一种可学习的预处理方法，用于处理对抗性多云图像，从而在不调整已部署的深度SOD模型的情况下，保持基于深度学习的遥感SOD模型性能。通过同时考虑常规和泛化对抗性示例，所提出的DefenseNet能够防御白盒设置下的对抗性云攻击以及黑盒设置下的其他攻击方法。在来自公开遥感SOD数据集（EORSSD）的合成基准上的实验结果表明，该方法对对抗性云攻击具有有效的防御能力。