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.
@article{arxiv.1809.05962,
title = {Robust Adversarial Perturbation on Deep Proposal-based Models},
author = {Yuezun Li and Daniel Tian and Ming-Ching Chang and Xiao Bian and Siwei Lyu},
journal= {arXiv preprint arXiv:1809.05962},
year = {2019}
}