  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.
