PEARL: Preprocessing Enhanced Adversarial Robust Learning of Image Deraining for Semantic Segmentation-Reference-Cited by-同舟云学术

PEARL: Preprocessing Enhanced Adversarial Robust Learning of Image Deraining for Semantic Segmentation

Published:2023-10-26 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 31st ACM International Conference on Multimedia
language:
Short-container-title:

Author:

Jiao Xianghao¹^ORCID,Liu Yaohua¹^ORCID,Gao Jiaxin¹^ORCID,Chu Xinyuan¹^ORCID,Fan Xin¹^ORCID,Liu Risheng¹^ORCID

Affiliation:

1. Dalian University of Technology, Dalian, China

Funder

the National Key R\&D Program of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3581783.3612164

Reference62 articles.

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2. On the Robustness of Semantic Segmentation Models to Adversarial Attacks

3. Anish Athalye and Nicholas Carlini . 2018. On the robustness of the cvpr 2018 white-box adversarial example defenses. arXiv preprint arXiv:1804.03286 ( 2018 ). Anish Athalye and Nicholas Carlini. 2018. On the robustness of the cvpr 2018 white-box adversarial example defenses. arXiv preprint arXiv:1804.03286 (2018).

4. On the Robustness of Redundant Teacher-Student Frameworks for Semantic Segmentation

5. Nicholas Carlini and David Wagner. 2017. Towards evaluating the robustness of neural networks. In 2017 ieee symposium on security and privacy (sp). Ieee 39--57. Nicholas Carlini and David Wagner. 2017. Towards evaluating the robustness of neural networks. In 2017 ieee symposium on security and privacy (sp). Ieee 39--57.

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