DPatch: Attacking Object Detectors with Adversarial Patches.

Object detectors have witnessed great progress in recent years and have been widely deployed in various important real-world scenarios, such as autonomous driving and face recognition. Therefore, it is increasingly vital to investigate the vulnerability of modern object detectors to different types of attacks. In this work, we demonstrate that actually many mainstream detectors (e.g. Faster R-CNN) can be hacked by a tiny adversarial patch. It is a non-trivial task since the original adversarial patch method can only be applied to image-level classifiers and is not capable to deal with the region proposals involved in modern detectors. Instead, here we iteratively evolve a tiny patch inside the input image so that it invalidates both proposal generation and the subsequent region classification of Faster R-CNN, resulting in a successful attack. Specifically, the proposed adversarial patch (namely, DPatch) can be trained toward any targeted class so that all the objects in any region of the scene will be classified as that targeted class. One interesting observation is that the efficiency of DPatch is not influenced by its location: no matter where it resides, the patch can always invalidate RCNN after the same amount of iterations. Furthermore, we find that different target classes have different degrees of vulnerability; and an DPatch with a larger size can perform the attack more effectively. Extensive experiments show that our DPatch can reduce the mAP of a state-of-the-art detector on PASCAL VOC 2012 from 71% to 25% and below.