Corpus ID: 208158412

AdvSPADE: Realistic Unrestricted Attacks for Semantic Segmentation

  title={AdvSPADE: Realistic Unrestricted Attacks for Semantic Segmentation},
  author={Guangyu Shen and Chengzhi Mao and Junfeng Yang and Baishakhi Ray},
  journal={arXiv: Computer Vision and Pattern Recognition},
  • Guangyu Shen, Chengzhi Mao, +1 author Baishakhi Ray
  • Published 2019
  • Computer Science, Engineering
  • arXiv: Computer Vision and Pattern Recognition
  • Due to the inherent robustness of segmentation models, traditional norm-bounded attack methods show limited effect on such type of models. In this paper, we focus on generating unrestricted adversarial examples for semantic segmentation models. We demonstrate a simple and effective method to generate unrestricted adversarial examples using conditional generative adversarial networks (CGAN) without any hand-crafted metric. The na\"ive implementation of CGAN, however, yields inferior image… CONTINUE READING
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