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Contextualized Perturbation for Textual Adversarial Attack

Introduction

This is a PyTorch implementation of Contextualized Perturbation for Textual Adversarial Attack by Dianqi Li, Yizhe Zhang, Hao Peng, Liqun Chen, Chris Brockett, Ming-Ting Sun and Bill Dolan, NAACL 2021.

A third-party implementation of CLARE is available in the TextAttack.

Environment

The code is based on python 3.6, tensorflow 1.14 and Pytorch 1.4.0 version. The code is developed and tested using one NVIDIA GTX 1080Ti.

Please use Conda to setup your environment, and then run

conda install -y pytorch==1.4.0 torchvision==0.5.0 cudatoolkit=10.1 -c pytorch

bash install_requirement.sh

Data Preparation and Pretrained Classifier

You can download pretrained target classifier and full training data in here (Note that the tartget claissifers are trained more robust than the classifers in TextFooler).

Alternatively, you can prepare you own training set in the same format as the example under /data/training_data/${dataset}/dataset/. The format will look like:

For single sentence classification, there is an empty field in text2.

After this, please run:

python train_BERT_classifier.py --dataset ${dataset} --save_model.

It will save pretrained classifer under the director: /saved_model/${dataset}_uncased/. The default target classifer is bert, you can train other types by setting extra argument: --target_model textcnn. Please check out the arguments in config.py for more details.

The text samples to be attacked are store in /data/${dataset}.tsv with the same format.

Textual Adversarial Attack

Simply run:

python bert_attack_classification.py --dataset ${dataset} --sample_file ${dataset}

and it will save the results under /adv_results/.

To attack qnli dataset, please add an argument --attack_second as we attack the longer sentence in two-sentence classification.

You can also modify the attacking hyper-parameters in hyper_parameters.py to adjust the trade-off between different aspects. Other details can be refered in config.py.

To run the attack from the baseline textfooler:

python attack_classification.py --dataset ${dataset} --sample_file ${dataset}

Citing

if you find our work is useful in your research, please consider citing:

@InProceedings{li2021contextualized,
  title={Contextualized perturbation for textual adversarial attack},
  author={Li, Dianqi and Zhang, Yizhe and Peng, Hao and Chen, Liqun and Brockett, Chris and Sun, Ming-Ting and Dolan, Bill},
  booktitle={Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics},
  year={2021}
}