Although the defense technology of text classification adversarial examples has achieved good results in the corresponding work, it is not effective in detecting word-level and sentence-level adversarial examples. Thus, how to use defense technology to im-prove the robustness of the target model ( Robust) has been the focus of academic community. This paper proposed a new algorithm to achieve defense against text classification adversarial examples. This method used the importance score of words and the detection of wrong words probabilities to locate the adversarial words in the sample. The results show that the classification accuracy of the target model increases from the original average of 14. 7 ％ to an average of 89. 2％, then the adversarial example defense techniques for text classification also improves.
维普
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