  Pretrained language models have significantly advanced performance across
various natural language processing tasks. However, adversarial attacks
continue to pose a critical challenge to system built using these models, as
they can be exploited with carefully crafted adversarial texts. Inspired by the
ability of diffusion models to predict and reduce noise in computer vision, we
propose a novel and flexible adversarial defense method for language
classification tasks, DiffuseDef, which incorporates a diffusion layer as a
denoiser between the encoder and the classifier. During inference, the
adversarial hidden state is first combined with sampled noise, then denoised
iteratively and finally ensembled to produce a robust text representation. By
integrating adversarial training, denoising, and ensembling techniques, we show
that DiffuseDef improves over different existing adversarial defense methods
and achieves state-of-the-art performance against common adversarial attacks.
