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booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
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number={15},
pages={13525--13533},
year={2021}
}
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year={2022}
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pages={12478--12497},
year={2022},
organization={PMLR}
}
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year={2020},
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year={2019}
}
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title={{Measure and Improve Robustness in NLP Models: A Survey}},
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journal={arXiv preprint arXiv:\href{https://arxiv.org/abs/2112.08313}{2112.08313}},
year={2021}
}
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title={\href{https://www.sciencedirect.com/science/article/pii/S0925231222003861}{{Adversarial attack and defense technologies in natural language processing: A survey}}},
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pages={278--307},
year={2022},
publisher={Elsevier}
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title={Deep text classification can be fooled},
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year={2017}
}
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year={2017}
}
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title={Adversarial texts with gradient methods},
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journal={arXiv preprint arXiv:\href{https://arxiv.org/abs/1801.07175}{1801.07175}},
year={2018}
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title={\href{https://proceedings.mlsys.org/paper/2019/hash/d1fe173d08e959397adf34b1d77e88d7-Abstract.html}{Discrete adversarial attacks and submodular optimization with applications to text classification}},
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pages={146--165},
year={2019}
}
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title={Textbugger: Generating adversarial text against real-world applications},
author={Li, Jinfeng and Ji, Shouling and Du, Tianyu and Li, Bo and Wang, Ting},
journal={arXiv preprint arXiv:\href{https://arxiv.org/abs/1812.05271}{1812.05271}},
year={2018}
}
@article{alzantot2018generating,
title={Generating natural language adversarial examples},
author={Alzantot, Moustafa and Sharma, Yash and Elgohary, Ahmed and Ho, Bo-Jhang and Srivastava, Mani and Chang, Kai-Wei},
journal={arXiv preprint arXiv:\href{https://arxiv.org/abs/1804.07998}{1804.07998}},
year={2018}
}
@inproceedings{ren2019generating,
title={\href{https://aclanthology.org/P19-1103/?amp=1}{Generating natural language adversarial examples through probability weighted word saliency}},
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booktitle={Proceedings of the 57th annual meeting of the association for computational linguistics},
pages={1085--1097},
year={2019}
}
@article{zang2019word,
title={Word-level textual adversarial attacking as combinatorial optimization},
author={Zang, Yuan and Qi, Fanchao and Yang, Chenghao and Liu, Zhiyuan and Zhang, Meng and Liu, Qun and Sun, Maosong},
journal={arXiv preprint arXiv:\href{https://arxiv.org/abs/1910.12196}{1910.12196}},
year={2019}
}
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title={Bae: Bert-based adversarial examples for text classification},
author={Garg, Siddhant and Ramakrishnan, Goutham},
journal={arXiv preprint arXiv:\href{https://arxiv.org/abs/2004.01970}{2004.01970}},
year={2020}
}
@inproceedings{jin2020bert,
title={\href{https://ojs.aaai.org/index.php/AAAI/article/view/6311}{Is bert really robust? a strong baseline for natural language attack on text classification and entailment}},
author={Jin, Di and Jin, Zhijing and Zhou, Joey Tianyi and Szolovits, Peter},
booktitle={Proceedings of the AAAI conference on artificial intelligence},
volume={34},
number={05},
pages={8018--8025},
year={2020}
}
@article{qin2022fuzzing,
title={\href{https://www.sciencedirect.com/science/article/pii/S016740482200092X}{{Fuzzing-based hard-label black-box attacks against machine learning models}}},
author={Qin, Yi and Yue, Chuan},
journal={Computers \& Security},
volume={117},
pages={102694},
year={2022},
publisher={Elsevier}
}
@article{li2020bert,
title={Bert-attack: Adversarial attack against bert using bert},
author={Li, Linyang and Ma, Ruotian and Guo, Qipeng and Xue, Xiangyang and Qiu, Xipeng},
journal={arXiv preprint arXiv:\href{https://arxiv.org/abs/2004.09984}{2004.09984}},
year={2020}
}
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title={\href{https://github.com/openai/gpt-2}{Language models are unsupervised multitask learners}},
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journal={OpenAI blog},
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number={8},
pages={9},
year={2019}
}
@article{maheshwary2021strong,
title={A Strong Baseline for Query Efficient Attacks in a Black Box Setting},
author={Maheshwary, Rishabh and Maheshwary, Saket and Pudi, Vikram},
journal={arXiv preprint arXiv:\href{https://arxiv.org/abs/2109.04775}{2109.04775}},
year={2021}
}
@article{saxena2020textdecepter,
title={Textdecepter: Hard label black box attack on text classifiers},
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year={2020}
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title={Thumbs up? Sentiment classification using machine learning techniques},
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@inproceedings{morris2020textattack,
title={\href{https://arxiv.org/abs/2005.05909}{TextAttack}: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP},
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year={2020}
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@inproceedings{lhoest-etal-2021-datasets,
title = "Datasets: A Community Library for Natural Language Processing",
author = "Lhoest, Quentin and
Villanova del Moral, Albert and
Jernite, Yacine and
Thakur, Abhishek and
von Platen, Patrick and
Patil, Suraj and
Chaumond, Julien and
Drame, Mariama and
Plu, Julien and
Tunstall, Lewis and
Davison, Joe and
{\v{S}}a{\v{s}}ko, Mario and
Chhablani, Gunjan and
Malik, Bhavitvya and
Brandeis, Simon and
Le Scao, Teven and
Sanh, Victor and
Xu, Canwen and
Patry, Nicolas and
McMillan-Major, Angelina and
Schmid, Philipp and
Gugger, Sylvain and
Delangue, Cl{\'e}ment and
Matussi{\`e}re, Th{\'e}o and
Debut, Lysandre and
Bekman, Stas and
Cistac, Pierric and
Goehringer, Thibault and
Mustar, Victor and
Lagunas, Fran{\c{c}}ois and
Rush, Alexander and
Wolf, Thomas",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-demo.21",
pages = "175--184",
abstract = "The scale, variety, and quantity of publicly-available NLP datasets has grown rapidly as researchers propose new tasks, larger models, and novel benchmarks. Datasets is a community library for contemporary NLP designed to support this ecosystem. Datasets aims to standardize end-user interfaces, versioning, and documentation, while providing a lightweight front-end that behaves similarly for small datasets as for internet-scale corpora. The design of the library incorporates a distributed, community-driven approach to adding datasets and documenting usage. After a year of development, the library now includes more than 650 unique datasets, has more than 250 contributors, and has helped support a variety of novel cross-dataset research projects and shared tasks. The library is available at https://github.com/huggingface/datasets.",
eprint={2109.02846},
archivePrefix={arXiv},
primaryClass={cs.CL},
}
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pages = "1746--1751",
}
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year={2018}
}
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pages={102730},
year={2022},
publisher={Elsevier}
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@INPROCEEDINGS{9897705,
author={Wang, Dan and Lin, Jiayu and Wang, Yuan-Gen},
booktitle={\href{https://ieeexplore.ieee.org/abstract/document/9897705}{2022 IEEE International Conference on Image Processing (ICIP)}},
title={{Query-Efficient Adversarial Attack Based On Latin Hypercube Sampling}},
year={2022},
volume={},
number={},
pages={546-550},
doi={10.1109/ICIP46576.2022.9897705}}
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