# Universal Adversarial Perturbations Against Semantic Image Segmentation

## TL;DR

This paper introduces universal adversarial perturbations for semantic image segmentation, demonstrating their ability to cause targeted or broad missegmentations with minimal perceptible noise, highlighting vulnerabilities in deep learning models.

## Contribution

It presents novel methods for generating universal adversarial perturbations specifically for semantic segmentation, extending prior work from image classification to more complex tasks.

## Key findings

- Existence of barely perceptible universal noise patterns that alter segmentation.
- Universal noise can remove specific classes from segmentation outputs.
- Perturbations are effective across diverse inputs.

## Abstract

While deep learning is remarkably successful on perceptual tasks, it was also shown to be vulnerable to adversarial perturbations of the input. These perturbations denote noise added to the input that was generated specifically to fool the system while being quasi-imperceptible for humans. More severely, there even exist universal perturbations that are input-agnostic but fool the network on the majority of inputs. While recent work has focused on image classification, this work proposes attacks against semantic image segmentation: we present an approach for generating (universal) adversarial perturbations that make the network yield a desired target segmentation as output. We show empirically that there exist barely perceptible universal noise patterns which result in nearly the same predicted segmentation for arbitrary inputs. Furthermore, we also show the existence of universal noise which removes a target class (e.g., all pedestrians) from the segmentation while leaving the segmentation mostly unchanged otherwise.