A Certified Radius-Guided Attack Framework to Image Segmentation Models. (arXiv:2304.02693v1 [cs.CV])

Image segmentation is an important problem in many safety-critical
applications. Recent studies show that modern image segmentation models are
vulnerable to adversarial perturbations, while existing attack methods mainly
follow the idea of attacking image classification models. We argue that image
segmentation and classification have inherent differences, and design an attack
framework specially for image segmentation models. Our attack framework is
inspired by certified radius, which was originally used by defenders to defend
against adversarial perturbations to classification models. We are …

adversarial applications attack attacker perspective certified classification critical defenders design framework important perspective problem radius safety safety-critical segmentation studies vulnerable