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

本文提出了一种基于对抗样本的方法，用于攻击人体实例分割网络，并提出了一种名为FashionAdv的新型对抗攻击框架，以最小化对图像质量的影响，该方法通过在原始图像中的服装区域上覆盖从时尚图像中学习到的对抗纹理，并通过对目标网络的多个组件进行联合攻击来增强方法的有效性。

基于时尚引导的人像分割对抗攻击