Generating Adversarial Examples in Chinese Texts Using Sentence-Pieces

By Linyang Li, Yunfan Shao, Demin Song, Xipeng Qiu, Xuanjing Huang. 2020

8 citations recorded by the source corpus.

Adversarial attacks in texts are mostly substitution-based methods that replace words or characters in the original texts to achieve success attacks. Recent methods use pre-trained language models as the substitutes generator. While in Chinese, such methods are not applicable since words in Chinese require segmentations first. In this paper, we propose a pre-train language model as the substitutes generator using sentence-pieces to craft adversarial examples in Chinese. The substitutions in the generated adversarial examples are not characters or words but \textit{'pieces'}, which are more natural to Chinese readers. Experiments results show that the generated adversarial samples can mislead strong target models and remain fluent and semantically preserved.

Subfields: Artificial Intelligence

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In Artificial Intelligence

1. Zhiyuan Liu
2. Minlie Huang
3. Maosong Sun
4. Jie Tang
5. Xu Han
6. Xipeng Qiu
7. Ji-rong Wen
8. Wayne Xin Zhao

Citations in the corpus

• Pre-Trained Models: Past, Present and Future (2021, methodology): “In single-stream models, such as VisualBERT (Li et al., 2019), Unicoder-VL (Li et al., 2020a), B2T2 (Alberti et al., 2019), the image region features and word embeddings are usually concatenated and fed into a single transformer.”