@inproceedings{gupta-etal-2023-dont,
    title = "Don{'}t Retrain, Just Rewrite: Countering Adversarial Perturbations by Rewriting Text",
    author = "Gupta, Ashim  and
      Blum, Carter  and
      Choji, Temma  and
      Fei, Yingjie  and
      Shah, Shalin  and
      Vempala, Alakananda  and
      Srikumar, Vivek",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.781",
    doi = "10.18653/v1/2023.acl-long.781",
    pages = "13981--13998",
    abstract = "Can language models transform inputs to protect text classifiers against adversarial attacks? In this work, we present ATINTER, a model that intercepts and learns to rewrite adversarial inputs to make them non-adversarial for a downstream text classifier. Our experiments on four datasets and five attack mechanisms reveal that ATINTER is effective at providing better adversarial robustness than existing defense approaches, without compromising task accuracy. For example, on sentiment classification using the SST-2 dataset, our method improves the adversarial accuracy over the best existing defense approach by more than 4{\%} with a smaller decrease in task accuracy (0.5 {\%} vs 2.5{\%}). Moreover, we show that ATINTER generalizes across multiple downstream tasks and classifiers without having to explicitly retrain it for those settings. For example, we find that when ATINTER is trained to remove adversarial perturbations for the sentiment classification task on the SST-2 dataset, it even transfers to a semantically different task of news classification (on AGNews) and improves the adversarial robustness by more than 10{\%}.",
}

Can language models transform inputs to protect text classifiers against adversarial attacks? In this work, we present ATINTER, a model that intercepts and learns to rewrite adversarial inputs to make them non-adversarial for a downstream text classifier. Our experiments on four datasets and five attack mechanisms reveal that ATINTER is effective at providing better adversarial robustness than existing defense approaches, without compromising task accuracy. For example, on sentiment classification using the SST-2 dataset, our method improves the adversarial accuracy over the best existing defense approach by more than 4{\%} with a smaller decrease in task accuracy (0.5 {\%} vs 2.5{\%}). Moreover, we show that ATINTER generalizes across multiple downstream tasks and classifiers without having to explicitly retrain it for those settings. For example, we find that when ATINTER is trained to remove adversarial perturbations for the sentiment classification task on the SST-2 dataset, it even transfers to a semantically different task of news classification (on AGNews) and improves the adversarial robustness by more than 10{\%}.
Gupta, Ashim Blum, Carter Choji, Temma Fei, Yingjie Shah, Shalin Vempala, Alakan a Srikumar, Vivek
Don't Retrain, Just Rewrite: Countering Adversarial Perturbations by Rewriting Text