Fashion-Guided Adversarial Attack on Person Segmentation

TL;DR

This paper introduces FashionAdv, a novel adversarial attack method that uses fashion-inspired textures to deceive person segmentation networks while maintaining natural appearance and robustness.

Contribution

It is the first to develop an adversarial attack specifically targeting person segmentation networks using fashion-guided textures.

Findings

FashionAdv effectively fools segmentation networks.

Generated textures are natural and inconspicuous.

Method shows robustness against image manipulations.

Abstract

This paper presents the first adversarial example based method for attacking human instance segmentation networks, namely person segmentation networks in short, which are harder to fool than classification networks. We propose a novel Fashion-Guided Adversarial Attack (FashionAdv) framework to automatically identify attackable regions in the target image to minimize the effect on image quality. It generates adversarial textures learned from fashion style images and then overlays them on the clothing regions in the original image to make all persons in the image invisible to person segmentation networks. The synthesized adversarial textures are inconspicuous and appear natural to the human eye. The effectiveness of the proposed method is enhanced by robustness training and by jointly attacking multiple components of the target network. Extensive experiments demonstrated the effectiveness of FashionAdv in terms of robustness to image manipulations and storage in cyberspace as well as appearing natural to the human eye. The code and data are publicly released on our project page https://github.com/nii-yamagishilab/fashion_adv