Kalt: generating adversarial explainable chinese legal texts

Deep neural networks (DNNs) are vulnerable to adversarial examples (AEs), which are well-designed input samples with imperceptible perturbations. Existing methods generate AEs to evaluate the robustness of DNN-based natural language processing models. However, the AE attack performance significantly degrades in some verticals, such as law, due to overlooking essential domain knowledge. To generate explainable Chinese legal adversarial texts, we introduce legal knowledge and propose a novel black-box approach, knowledge-aware law tricker (KALT), in the framework of adversarial text generation based on word importance. Firstly, we invent a legal knowledge extraction method based on KeyBERT. The knowledge contains unique features from each category and shared features among different categories. Additionally, we design two perturbation strategies, Strengthen Similar Label and Weaken Original Label, to selectively perturb the two types of features, which can significantly reduce the classification accuracy of the target model. These two perturbation strategies can be regarded as components, which can be conveniently integrated into any perturbation method to enhance attack performance. Furthermore, we propose a strong hybrid perturbation method to introduce perturbation into the original texts. The perturbation method combines seven representative perturbation methods for Chinese. Finally, we design a formula to calculate interpretability scores, quantifying the interpretability of adversarial text generation methods. Experimental results demonstrate that KALT can effectively generate explainable Chinese legal adversarial texts that can be misclassified with high confidence and achieve excellent attack performance against the powerful Chinese BERT.