Recent researches show that the deep learning based object detection is vulnerable to adversarial examples. Generally, the adversarial attack for object detection contains targeted attack and untargeted attack. According to our detailed investigations, the research on the former is relatively fewer than the latter and all the existing methods for the targeted attack follow the same mode, i.e., the object-mislabeling mode that misleads detectors to mislabel the detected object as a specific wrong label. However, this mode has limited attack success rate, universal and generalization performances. In this paper, we propose a new object-fabrication targeted attack mode which can mislead detectors to `fabricate' extra false objects with specific target labels. Furthermore, we design a dual attention based targeted feature space attack method to implement the proposed targeted attack mode. The attack performances of the proposed mode and method are evaluated on MS COCO and BDD100K datasets using FasterRCNN and YOLOv5. Evaluation results demonstrate that, the proposed object-fabrication targeted attack mode and the corresponding targeted feature space attack method show significant improvements in terms of image-specific attack, universal performance and generalization capability, compared with the previous targeted attack for object detection. Code will be made available.
翻译:根据我们的详细调查,对前者的研究相对较少,对定向攻击的所有现有方法都遵循同样的模式,即,使探测器误将已发现物体贴上特定错误标签的物体贴标签模式。然而,这一模式限制了攻击成功率、普遍性和通用性能。在本文件中,我们提议一种新的物体制造定向攻击模式,该模式可以误导探测器,以“制造”带有特定目标标签的额外假物体。此外,我们设计了一种基于双重关注的定向空间攻击方法,以实施拟议的定向攻击模式。拟议模式和方法的攻击性能根据MS COCO和BDD100K数据集评价,使用WeperRCNN和YOLOv5.评价结果显示,拟议的目标制造攻击模式和相应的定向空间攻击方法将显示在特定图像攻击、普遍性能和通用性能方面有显著改进,与以往的定向攻击探测能力相比,将显示针对特定目标的攻击标准。