Advanced Patch Attacks (PAs) on object detection in natural images have pointed out the great safety vulnerability in methods based on deep neural networks. However, little attention has been paid to this topic in Optical Remote Sensing Images (O-RSIs). To this end, we focus on this research, i.e., PAs on object detection in O-RSIs, and propose a more Threatening PA without the scarification of the visual quality, dubbed TPA. Specifically, to address the problem of inconsistency between local and global landscapes in existing patch selection schemes, we propose leveraging the First-Order Difference (FOD) of the objective function before and after masking to select the sub-patches to be attacked. Further, considering the problem of gradient inundation when applying existing coordinate-based loss to PAs directly, we design an IoU-based objective function specific for PAs, dubbed Bounding box Drifting Loss (BDL), which pushes the detected bounding boxes far from the initial ones until there are no intersections between them. Finally, on two widely used benchmarks, i.e., DIOR and DOTA, comprehensive evaluations of our TPA with four typical detectors (Faster R-CNN, FCOS, RetinaNet, and YOLO-v4) witness its remarkable effectiveness. To the best of our knowledge, this is the first attempt to study the PAs on object detection in O-RSIs, and we hope this work can get our readers interested in studying this topic.

翻译：针对自然图像中目标检测的高级补丁攻击（PAs）已指出基于深度神经网络的方法存在巨大安全漏洞。然而，光学遥感图像（O-RSIs）中该主题鲜受关注。为此，我们聚焦于O-RSIs中目标检测的PAs研究，提出一种在不牺牲视觉质量前提下更具威胁性的补丁攻击（TPA）。具体而言，针对现有补丁选择方案中局部与全局景观不一致的问题，我们提出利用掩蔽前后目标函数的一阶差分（FOD）来选择待攻击子补丁。进一步，考虑到直接将现有基于坐标的损失函数应用于PAs时存在的梯度淹没问题，我们设计了一种专用于PAs的IoU基目标函数——边界框漂移损失（BDL），该函数推动检测框远离初始位置直至二者无交集。最后，在两个广泛使用的基准数据集（DIOR和DOTA）上，使用四种典型检测器（Faster R-CNN、FCOS、RetinaNet和YOLO-v4）对TPA的全面评估验证了其显著有效性。据我们所知，这是首次尝试研究O-RSIs中目标检测的PAs，希望本工作能激发读者对该主题的研究兴趣。