Source code for our paper Frequency-Guided Word Substitutions for Detecting Textual Adversarial Examples (EACL 2021).
The code is built in Python 3.7.7. To install all required packages, run
pip install -r requirements.txt
To download the necessary datasets and pre-trained embeddings, run
cd data
sh download_data.sh
Now you can train a model by running
python3 main.py -dataset imdb -model_type roberta -gpu
The -gpu
flag should be removed when no GPU is available. See config.py
for other dataset and model options.
A trained model can be tested by running
python3 main.py -dataset imdb -model_type roberta -gpu -mode test
Once you have trained a model, run
python3 attack_models.py -mode attack -dataset imdb -model_type roberta -gpu -limit 2000 -attack random
The -gpu
flag should be removed when no GPU is available. See config.py
for other dataset, attack and model options.
Finally, adversarial sequence detection can be done.
First, for FGWS we need to tune delta on the validation set. Run
python3 attack_models.py -mode attack -dataset imdb -model_type roberta -gpu -limit 2000 -attack prioritized -attack_val_set
python3 detect.py -mode detect -dataset imdb -model_type roberta -gpu -limit 2000 -attack prioritized -fp_threshold 0.9 -tune_delta_on_val
to tune the parameter.
For testing with FGWS, run
python3 detect.py -mode detect -dataset imdb -model_type roberta -gpu -limit 2000 -attack random -fp_threshold 0.9
For NWS, run
python3 detect.py -mode detect -dataset imdb -model_type roberta -gpu -limit 2000 -attack random -fp_threshold 0.9 -detect_baseline
If you find this repository useful, please consider citing our paper:
@inproceedings{mozes-etal-2021-frequency,
title = "Frequency-Guided Word Substitutions for Detecting Textual Adversarial Examples",
author = "Mozes, Maximilian and
Stenetorp, Pontus and
Kleinberg, Bennett and
Griffin, Lewis",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2021.eacl-main.13",
pages = "171--186"
}