  Adversarial training is the most empirically successful approach in improving
the robustness of deep neural networks for image classification.For text
classification, however, existing synonym substitution based adversarial
attacks are effective but not efficient to be incorporated into practical text
adversarial training. Gradient-based attacks, which are very efficient for
images, are hard to be implemented for synonym substitution based text attacks
due to the lexical, grammatical and semantic constraints and the discrete text
input space. Thereby, we propose a fast text adversarial attack method called
Fast Gradient Projection Method (FGPM) based on synonym substitution, which is
about 20 times faster than existing text attack methods and could achieve
similar attack performance. We then incorporate FGPM with adversarial training
and propose a text defense method called Adversarial Training with FGPM
enhanced by Logit pairing (ATFL). Experiments show that ATFL could
significantly improve the model robustness and block the transferability of
adversarial examples.
