面向中文文本分类的字符级对抗样本生成方法

Character-level Adversarial Samples Generation Approach for Chinese Text Classification

对抗样本生成是一种通过添加较小扰动信息,使得神经网络产生误判的技术,可用于检测文本分类模型的鲁棒性。目前,中文领域对抗样本生成方法主要有繁体字和同音字替换等,这些方法都存在对抗样本扰动幅度大,生成对抗样本质量不高的问题。针对这些问题,该文提出一种字符级对抗样本生成方法(PGAS),通过对多音字进行替换可以在较小扰动下生成高质量的对抗样本。首先,构建多音字字典,对多音字进行标注;然后对输入文本进行多音字替换;最后在黑盒模式下进行对抗样本攻击实验。实验在多种情感分类数据集上,针对多种最新的分类模型验证了该方法的有效性。

Adversarial sample generation is a technique that makes the neural network produce misjudgments by adding small disturbance information.Which can be used to detect the robustness of text classification models.At present,the methods of sample generation in the Chinese domain mainly include traditional characters and homophones substitution,which have the problems of large disturbance amplitude of sample generation and low quality of sample generation.Polyphonic characters Generation Adversarial Sample(PGAS),a character-level countermeasure samples generation approach,is proposed in this paper.Which can generate high-quality adversarial samples with minor disturbance by replacing polyphonic characters.First,a polyphonic word dictionary to label polyphonic words is constructed.Then,the input text with polyphonic words is replaced.Finally,an adversarial sample attack experiment in the black-box model is conducted.Experiments on multiple sentiment classification datasets verify the effectiveness of the proposed method for a variety of the latest classification models.