SCAD: Subspace Clustering based Adversarial Detector-Reference-Cited by-同舟云学术

SCAD: Subspace Clustering based Adversarial Detector

Published:2024-03-04 Issue: Volume:1 Page:286-294
ISSN:
Container-title:Proceedings of the 17th ACM International Conference on Web Search and Data Mining
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Author:

Hu Xinrong¹^ORCID,Chen Wushuan¹^ORCID,Yang Jie²^ORCID,Guo Yi³^ORCID,Yao Xun¹^ORCID,Wang Bangchao¹^ORCID,Liu Junping¹^ORCID,Xu Ce¹^ORCID

Affiliation:

1. School of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan, Hubei, China

2. School of Computing and Information Technology, University of Wollongong, Wollongong, NSW, Australia

3. School of Computer, Data and Mathematical Sciences, Western Sydney University, Parramatta, NSW, Australia

Funder

Telstra-UOW Hub for AIOT Solutions Seed Funding, 2023

Australian Research Council Discovery Project

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3616855.3635835

Reference32 articles.

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2. Black-Box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers

3. SongYang Gao, Shihan Dou, Qi Zhang, Xuanjing Huang, Jin Ma, and Ying Shan. 2023. On the Universal Adversarial Perturbations for Efficient Data-free Adversarial Detection. In Findings of the Association for Computational Linguistics: ACL 2023. Toronto, Canada, 13573--13581.

4. BAE: BERT-based Adversarial Examples for Text Classification

5. Spatial subspace clustering for drill hole spectral data