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        <title>Generating Textual Adversaries with Minimal Perturbation</title>
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    <name type="personal">
        <namePart type="given">Shuhan</namePart>
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    <abstract>Many word-level adversarial attack approaches for textual data have been proposed in recent studies. However, due to the massive search space consisting of combinations of candidate words, the existing approaches face the problem of preserving the semantics of texts when crafting adversarial counterparts. In this paper, we develop a novel attack strategy to find adversarial texts with high similarity to the original texts while introducing minimal perturbation. The rationale is that we expect the adversarial texts with small perturbation can better preserve the semantic meaning of original texts. Experiments show that, compared with state-of-the-art attack approaches, our approach achieves higher success rates and lower perturbation rates in four benchmark datasets.</abstract>
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    <identifier type="doi">10.18653/v1/2022.findings-emnlp.337</identifier>
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%0 Conference Proceedings
%T Generating Textual Adversaries with Minimal Perturbation
%A Zhao, Xingyi
%A Zhang, Lu
%A Xu, Depeng
%A Yuan, Shuhan
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F zhao-etal-2022-generating
%X Many word-level adversarial attack approaches for textual data have been proposed in recent studies. However, due to the massive search space consisting of combinations of candidate words, the existing approaches face the problem of preserving the semantics of texts when crafting adversarial counterparts. In this paper, we develop a novel attack strategy to find adversarial texts with high similarity to the original texts while introducing minimal perturbation. The rationale is that we expect the adversarial texts with small perturbation can better preserve the semantic meaning of original texts. Experiments show that, compared with state-of-the-art attack approaches, our approach achieves higher success rates and lower perturbation rates in four benchmark datasets.
%R 10.18653/v1/2022.findings-emnlp.337
%U https://aclanthology.org/2022.findings-emnlp.337
%U https://doi.org/10.18653/v1/2022.findings-emnlp.337
%P 4599-4606

Markdown (Informal)

[Generating Textual Adversaries with Minimal Perturbation](https://aclanthology.org/2022.findings-emnlp.337) (Zhao et al., Findings 2022)

• Generating Textual Adversaries with Minimal Perturbation (Zhao et al., Findings 2022)

ACL

• Xingyi Zhao, Lu Zhang, Depeng Xu, and Shuhan Yuan. 2022. Generating Textual Adversaries with Minimal Perturbation. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 4599–4606, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.