Certified Robustness to Text Adversarial Attacks by Randomized [MASK]

• URL: http://arxiv.org/abs/2105.03743v1
• Date: Sat, 8 May 2021 16:59:10 GMT
• Title: Certified Robustness to Text Adversarial Attacks by Randomized [MASK]
• Authors: Jiehang Zeng, Xiaoqing Zheng, Jianhan Xu, Linyang Li, Liping Yuan and Xuanjing Huang
• Abstract summary: We propose a certifiably robust defense method by randomly masking a certain proportion of the words in an input text. The proposed method can defend against not only word substitution-based attacks, but also character-level perturbations. We can certify the classifications of over 50% texts to be robust to any perturbation of 5 words on AGNEWS, and 2 words on SST2 dataset.
• Score: 39.07743913719665
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recently, few certified defense methods have been developed to provably guarantee the robustness of a text classifier to adversarial synonym substitutions. However, all existing certified defense methods assume that the defenders are informed of how the adversaries generate synonyms, which is not a realistic scenario. In this paper, we propose a certifiably robust defense method by randomly masking a certain proportion of the words in an input text, in which the above unrealistic assumption is no longer necessary. The proposed method can defend against not only word substitution-based attacks, but also character-level perturbations. We can certify the classifications of over 50% texts to be robust to any perturbation of 5 words on AGNEWS, and 2 words on SST2 dataset. The experimental results show that our randomized smoothing method significantly outperforms recently proposed defense methods across multiple datasets.

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