Semantic-Preserving Adversarial Text Attacks

Author:

Yang Xinghao1ORCID,Gong Yongshun2,Liu Weifeng1ORCID,Bailey James3ORCID,Tao Dacheng4ORCID,Liu Wei5ORCID

Affiliation:

1. College of Control Science, Engineering, China University of Petroleum (East China), Qingdao, China

2. School of Software, Shandong University, Jinan

3. Department of Computing, Information Systems, University of Melbourne, Australia

4. Faculty of Engineering, The University of Sydney, Darlington, Sydney, NSW, Australia

5. School of Computer Science, University of Technology Sydney, 15 Broadway, Sydney, NSW, Australia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Control and Optimization,Hardware and Architecture,Renewable Energy, Sustainability and the Environment,Software

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Investigating the Robustness of Arabic Offensive Language Transformer-Based Classifiers to Adversarial Attacks;2024 Intelligent Methods, Systems, and Applications (IMSA);2024-07-13

2. Jointly Optimized Classifiers for Few-Shot Class-Incremental Learning;IEEE Transactions on Emerging Topics in Computational Intelligence;2024

3. Towards Unsupervised Graph Completion Learning on Graphs with Features and Structure Missing;2023 IEEE International Conference on Data Mining (ICDM);2023-12-01

4. Security and Privacy Preservation Method Based on Data Disturbance for Collaborative Edge Computing for Social-Internet-of-Things (S-IoT);2023 International Conference on Integrated Intelligence and Communication Systems (ICIICS);2023-11-24

5. Analysis of Different Adversarial Attacks on Various NLP SoTA Models;2023 International Conference on Computational Intelligence for Information, Security and Communication Applications (CIISCA);2023-06-22

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