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 …