关键词: 对抗样本, 中文文本, 神经网络, 黑盒攻击, 深度学习

Abstract: Deep learning models have been widely used to deal with natural language tasks, but the latest research shows that adversarial attacks will seriously reduce the accuracy of the classification model and make the model classification function ineffective. Aiming at the vulnerability of deep learning models when dealing with natural language tasks, a new adversarial examples generation method, KeywordsAttack, is proposed. The method uses a statistical algorithm to select some words to form a text keyword set. And then it iteratively replaces the keywords according to the contribution of the model classification results until the classification model is successfully misled or the number of replacements reaches the set value. According to the characteristics of Chinese, this method generates adversarial examples by splitting Chinese characters and replacing pinyin. Finally, using the public hotel shopping review dataset to conduct experiments, the results show that the average modification magnitude of adversarial examples accounts for 18.2% of the original text and the classification accuracy of attacking the BERT model is reduced by about 43%, and the classification accuracy of attacking the LSTM model is reduced by about 30%. These data show that the KeywordsAttack method can successfully mislead the classification model by making small perturbations to the text. At the same time, the number of query models in the process of generating adversarial examples is small.

Key words: adversarial examples, Chinese text, neural network, blackbox attack, deep learning