Character-level Adversarial Samples Generation Approach for Chinese Text Classification

ZHANG Shunxiang; WU Houyue; ZHU Guangli; Xu Xin; SU Mingxing

doi:10.11999/JEIT220563

Volume 45 Issue 6

Jun. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(6): 2226-2235

ZHANG Shunxiang, WU Houyue, ZHU Guangli, Xu Xin, SU Mingxing. Character-level Adversarial Samples Generation Approach for Chinese Text Classification[J]. Journal of Electronics & Information Technology, 2023, 45(6): 2226-2235. doi: 10.11999/JEIT220563

Citation:

ZHANG Shunxiang, WU Houyue, ZHU Guangli, Xu Xin, SU Mingxing. Character-level Adversarial Samples Generation Approach for Chinese Text Classification[J]. Journal of Electronics & Information Technology, 2023, 45(6): 2226-2235. doi: 10.11999/JEIT220563

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Character-level Adversarial Samples Generation Approach for Chinese Text Classification

doi: 10.11999/JEIT220563

1.
School of Computer Science and Engineering, Anhui University of Science & Technology, Huainan 232001, China
2.
Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China

Funds: The National Natural Science Foundation of China (62076006), The University Synergy Innovation Program of Anhui Province (GXXT-2021-008), The Graduate Students Scientific Research Project of Anhui Province(YJS20210402)

Received Date: 2022-05-07
Rev Recd Date: 2022-07-09

Available Online: 2022-07-14

Publish Date: 2023-06-10

Abstract

Abstract

Adversarial sample generation is a technique that makes the neural network produce misjudgments by adding small disturbance information. Which can be used to detect the robustness of text classification models. At present, the methods of sample generation in the Chinese domain include mainly traditional characters and homophones substitution, which have the problems of large disturbance amplitude of sample generation and low quality of sample generation. Polyphonic characters Generation Adversarial Sample (PGAS), a character-level countermeasure samples generation approach, is proposed in this paper. Which can generate high-quality adversarial samples with minor disturbance by replacing polyphonic characters. First, a polyphonic word dictionary to label polyphonic words is constructed. Then, the input text with polyphonic words is replaced. Finally, an adversarial sample attack experiment in the black-box model is conducted. Experiments on multiple sentiment classification datasets verify the effectiveness of the proposed method for a variety of the latest classification models.
- Anti-sample generation,
- Text classification,
- Sentimental classification,
- Polyphonic characters,
- Character-level adversarial samples

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References(41)

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