Robust Adversarial Perturbation on Deep Proposal-based Models

Understand

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.