Physical adversarial attacks against object detectors have seen increasing\nsuccess in recent years. However, these attacks require direct access to the\nobject of interest in order to apply a physical patch. Furthermore, to hide\nmultiple objects, an adversarial patch must be applied to each object. In this\npaper, we propose a contactless translucent physical patch containing a\ncarefully constructed pattern, which is placed on the camera's lens, to fool\nstate-of-the-art object detectors. The primary goal of our patch is to hide all\ninstances of a selected target class. In addition, the optimization method used\nto construct the patch aims to ensure that the detection of other (untargeted)\nclasses remains unharmed. Therefore, in our experiments, which are conducted on\nstate-of-the-art object detection models used in autonomous driving, we study\nthe effect of the patch on the detection of both the selected target class and\nthe other classes. We show that our patch was able to prevent the detection of\n42.27% of all stop sign instances while maintaining high (nearly 80%) detection\nof the other classes.\n