  Adversarial noises are useful tools to probe the weakness of deep learning
based computer vision algorithms. In this paper, we describe a robust
adversarial perturbation (R-AP) method to attack deep proposal-based object
detectors and instance segmentation algorithms. Our method focuses on attacking
the common component in these algorithms, namely Region Proposal Network (RPN),
to universally degrade their performance in a black-box fashion. To do so, we
design a loss function that combines a label loss and a novel shape loss, and
optimize it with respect to image using a gradient based iterative algorithm.
Evaluations are performed on the MS COCO 2014 dataset for the adversarial
attacking of 6 state-of-the-art object detectors and 2 instance segmentation
algorithms. Experimental results demonstrate the efficacy of the proposed
method.
