Defense of Word-level Adversarial Attacks via Random Substitution Encoding

• URL: http://arxiv.org/abs/2005.00446v2
• Date: Fri, 12 Jun 2020 05:55:34 GMT
• Title: Defense of Word-level Adversarial Attacks via Random Substitution Encoding
• Authors: Zhaoyang Wang and Hongtao Wang
• Abstract summary: adversarial attacks against deep neural networks on computer vision tasks have spawned many new technologies that help protect models from avoiding false predictions. Recently, word-level adversarial attacks on deep models of Natural Language Processing (NLP) tasks have also demonstrated strong power, e.g., fooling a sentiment classification neural network to make wrong decisions. We propose a novel framework called Random Substitution RSE, which introduces a random substitution into the training process of original neural networks.
• Score: 0.5964792400314836
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The adversarial attacks against deep neural networks on computer vision tasks have spawned many new technologies that help protect models from avoiding false predictions. Recently, word-level adversarial attacks on deep models of Natural Language Processing (NLP) tasks have also demonstrated strong power, e.g., fooling a sentiment classification neural network to make wrong decisions. Unfortunately, few previous literatures have discussed the defense of such word-level synonym substitution based attacks since they are hard to be perceived and detected. In this paper, we shed light on this problem and propose a novel defense framework called Random Substitution Encoding (RSE), which introduces a random substitution encoder into the training process of original neural networks. Extensive experiments on text classification tasks demonstrate the effectiveness of our framework on defense of word-level adversarial attacks, under various base and attack models.

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