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A line of work has shown that natural text processing models are vulnerable to adversarial examples.
WordNet: A lexical database for English
George A. Miller · 1992
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Learning word vectors for sentiment analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts · 2011
Earlier work this paper cites.
Convolutional neural networks for sentence classification
Yoon Kim · 2014
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
Earlier work this paper cites.
Character-level convolutional networks for text classification
Xiang Zhang, Junbo Jake Zhao, and Yann LeCun · 2015
Earlier work this paper cites.
Counter-fitting word vectors to linguistic constraints
Nikola Mrkšić, Diarmuid Ó Séaghdha, Blaise Thomson, Milica Gašić, Lina M. Rojas-Barahona, Pei-Hao Su, David Vandyke, Tsung-Hsien Wen, and Steve Young · 2016
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Crafting adversarial input sequences for recurrent neural networks
Nicolas Papernot, Patrick D. McDaniel, Ananthram Swami, and Richard E. Harang · 2016
Earlier work this paper cites.
Safetynet: Detecting and rejecting adversarial examples robustly
Jiajun Lu, Theerasit Issaranon, and David A. Forsyth · 2017
Earlier work this paper cites.
Generating natural language adversarial examples
Moustafa Alzantot, Yash Sharma, Ahmed Elgohary, Bo-Jhang Ho, Mani Srivastava, and Kai-Wei Chang · 2018
Earlier work this paper cites.
HotFlip: White-box adversarial examples for text classification
Javid Ebrahimi, Anyi Rao, Daniel Lowd, and Dejing Dou · 2018
Earlier work this paper cites.
Black-box generation of adversarial text sequences to evade deep learning classifiers
Ji Gao, Jack Lanchantin, Mary Lou Soffa, and Yanjun Qi · 2018
Earlier work this paper cites.
Adversarial example generation with syntactically controlled paraphrase networks
Mohit Iyyer, John Wieting, Kevin Gimpel, and Luke Zettlemoyer · 2018
Earlier work this paper cites.
Deep text classification can be fooled
Bin Liang, Hongcheng Li, Miaoqiang Su, Pan Bian, Xirong Li, and Wenchang Shi · 2018
Earlier work this paper cites.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
Cited alongside, same era.
Semantically equivalent adversarial rules for debugging NLP models
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2018
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Cited alongside, same era.
Achieving verified robustness to symbol substitutions via interval bound propagation
Po-Sen Huang, Robert Stanforth, Johannes Welbl, Chris Dyer, Dani Yogatama, Sven Gowal, Krishnamurthy Dvijotham, and Pushmeet Kohli · 2019
Cited alongside, same era.
Certified robustness to adversarial word substitutions
Robin Jia, Aditi Raghunathan, Kerem Göksel, and Percy Liang · 2019
Cited alongside, same era.
BERT-ATTACK: Adversarial attack against BERT using BERT
Linyang Li, Ruotian Ma, Qipeng Guo, Xiangyang Xue, and Xipeng Qiu · 2020
Later among the works it cites.
Joint character-level word embedding and adversarial stability training to defend adversarial text
Hui Liu, Yongzheng Zhang, Yipeng Wang, Zheng Lin, and Yige Chen · 2020
Later among the works it cites.
Robustness verification for transformers
Zhouxing Shi, Huan Zhang, Kai-Wei Chang, Minlie Huang, and Cho-Jui Hsieh · 2020
Later among the works it cites.
T3: Tree-autoencoder constrained adversarial text generation for targeted attack
Boxin Wang, Hengzhi Pei, Boyuan Pan, Qian Chen, Shuohang Wang, and Bo Li · 2020
Later among the works it cites.
Word-level textual adversarial attacking as combinatorial optimization
Yuan Zang, Fanchao Qi, Chenghao Yang, Zhiyuan Liu, Meng Zhang, Qun Liu, and Maosong Sun · 2020
Later among the works it cites.
Towards robustness against natural language word substitutions
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Jinfeng Li, Shouling Ji, Tianyu Du, Bo Li, and Ting Wang · 2019
Cited alongside, same era.
Roberta: A robustly optimized BERT pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
Cited alongside, same era.
Combating adversarial misspellings with robust word recognition
Danish Pruthi, Bhuwan Dhingra, and Zachary C. Lipton · 2019
Cited alongside, same era.
Generating natural language adversarial examples through probability weighted word saliency
Shuhuai Ren, Yihe Deng, Kun He, and Wanxiang Che · 2019
Cited alongside, same era.
PAWS: Paraphrase adversaries from word scrambling
Yuan Zhang, Jason Baldridge, and Luheng He · 2019
Cited alongside, same era.
Learning to discriminate perturbations for blocking adversarial attacks in text classification
Yichao Zhou, Jyun-Yu Jiang, Kai-Wei Chang, and Wei Wang · 2019
Cited alongside, same era.
BAE: BERT-based adversarial examples for text classification
Siddhant Garg and Goutham Ramakrishnan · 2020
Cited alongside, same era.
Xinshuai Dong, Anh Tuan Luu, Rongrong Ji, and Hong Liu · 2021
Closest in time.
Gradient-based adversarial attacks against text transformers
Chuan Guo, Alexandre Sablayrolles, Hervé Jégou, and Douwe Kiela · 2021
Closest in time.
Generating natural language attacks in a hard label black box setting
Rishabh Maheshwary, Saket Maheshwary, and Vikram Pudi · 2021
Closest in time.
Frequency-guided word substitutions for detecting textual adversarial examples
Maximilian Mozes, Pontus Stenetorp, Bennett Kleinberg, and Lewis D Griffin · 2021
Closest in time.
Better robustness by more coverage: Adversarial training with mixup augmentation for robust fine-tuning
Chenglei Si, Zhengyan Zhang, Fanchao Qi, Zhiyuan Liu, Yasheng Wang, Qun Liu, and Maosong Sun · 2021
Closest in time.
Enhancing the transferability of adversarial attacks through variance tuning
Xiaosen Wang and Kun He · 2021
Closest in time.
On the transferability of adversarial attacks against neural text classifier
Liping Yuan, Xiaoqing Zheng, Yi Zhou, Cho-Jui Hsieh, and Kai-Wei Chang · 2021
Closest in time.
Defense against adversarial attacks in nlp via dirichlet neighborhood ensemble
Yi Zhou, Xiaoqing Zheng, Cho-Jui Hsieh, Kai-wei Chang, and Xuanjing Huang · 2021
Closest in time.
Robust textual embedding against word-level adversarial attacks
Yichen Yang, Xiaosen Wang, and Kun He · 2022
Closest in time.