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BERT-Defense: A Probabilistic Model Based on BERT to Combat Cognitively Inspired Orthographic Adversarial Attacks

Code and data for our paper

@inproceedings{keller-etal-2021-bert,
    title = "{BERT}-Defense: A Probabilistic Model Based on {BERT} to Combat Cognitively Inspired Orthographic Adversarial Attacks",
    author = "Keller, Yannik  and
      Mackensen, Jan  and
      Eger, Steffen",
    booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.findings-acl.141",
    doi = "10.18653/v1/2021.findings-acl.141",
    pages = "1616--1629",
}

Requirements

There is an environment.yml provided.
To summarize, the main packages are:

• python >= 3.6
• numpy
• torch==1.5.0
• nltk
• tqdm
• pillow
• pytorch_pretrained_bert
• pandas

Usage

You can use BERT-Defense to clean a single sentence using the script at bayesian_shielding/frontend/clean_sentences.py. Run for example python clean_sentences.py "Egyp's Muslim BrotherhooRefuse toBackDown" to clean the sentence and obtain the output egypt muslim brotherhood refuse to back down.

If you want to clean multiple sentences, you should probably make use of batched processing and use the evaluation/clean_document.py script. However, currently, you will need to format your document of sentences in a format similar to the files in evaluation/attacked_documents.

Notes

If you cite the Zéroe benchmark, please also cite this paper:

@inproceedings{eger-benz-2020-hero,
    title = "From Hero to Z{\'e}roe: A Benchmark of Low-Level Adversarial Attacks",
    author = "Eger, Steffen  and
      Benz, Yannik",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.79",
    pages = "786--803",
    abstract = "Adversarial attacks are label-preserving modifications to inputs of machine learning classifiers designed to fool machines but not humans. Natural Language Processing (NLP) has mostly focused on high-level attack scenarios such as paraphrasing input texts. We argue that these are less realistic in typical application scenarios such as in social media, and instead focus on low-level attacks on the character-level. Guided by human cognitive abilities and human robustness, we propose the first large-scale catalogue and benchmark of low-level adversarial attacks, which we dub Z{\'e}roe, encompassing nine different attack modes including visual and phonetic adversaries. We show that RoBERTa, NLP{'}s current workhorse, fails on our attacks. Our dataset provides a benchmark for testing robustness of future more human-like NLP models.",
}