MPAT: Building Robust Deep Neural Networks against Textual Adversarial
  Attacks
Fangyuan Zhang Huichi Zhou Shuangjiao Li Hongtao Wang
Deep neural networks have been proven to be vulnerable to adversarial
examples and various methods have been proposed to defend against adversarial
attacks for natural language processing tasks. However, previous defense
methods have limitations in maintaining effective defense while ensuring the
performance of the original task. In this paper, we propose a malicious
perturbation based adversarial training method (MPAT) for building robust deep
neural networks against textual adversarial attacks. Specifically, we construct
a multi-level malicious example generation strategy to generate adversarial
examples with malicious perturbations, which are used instead of original
inputs for model training. Additionally, we employ a novel training objective
function to ensure achieving the defense goal without compromising the
performance on the original task. We conduct comprehensive experiments to
evaluate our defense method by attacking five victim models on three benchmark
datasets. The result demonstrates that our method is more effective against
malicious adversarial attacks compared with previous defense methods while
maintaining or further improving the performance on the original task.