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Recently, few certified defense methods have been developed to provably guarantee the robustness of a text classifier to adversarial synonym substitutions.
A survey: Towards a robust deep neural network in text domain
Wenqi Wang, Lina Wang, Benxiao Tang, Run Wang, and Aoshuang Ye. 2019 · 1902
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Interpretable adversarial training for text
Samuel Barham and Soheil Feizi. 2019 · 1905
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Roberta: A robustly optimized bert pretraining approach
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Universal adversarial triggers for NLP
Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, and Sameer Singh. 2019 · 1908
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Adversarial attacks and defenses in images, graphs and text: A review
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Defense against adversarial attacks in nlp via dirichlet neighborhood ensemble
Yi Zhou, Xiaoqing Zheng, Cho-Jui Hsieh, Kai-wei Chang, and Xuanjing Huang. 2020 · 2006
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Voting based ensemble improves robustness of defensive models
Minhao Cheng, Cho-Jui Hsieh, Inderjit Dhillon, et al. 2020a · 2011
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Learning word vectors for sentiment analysis
Maas andrew L., Raymond E. Daly, Peter T. Pham, Dan Huang, Ng andrew Y., and Christopher Potts. 2011 · 2011
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The MNIST database of handwritten digit images for machine learning research [best of the web]
Li Deng. 2012 · 2012
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Generating natural language attacks in a hard label black box setting
Rishabh Maheshwary, Saket Maheshwary, and Vikram Pudi. 2020 · 2012
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Ng andrew, and Christopher Potts. 2013 · 2013
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GloVe: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus. 2014 · 2014
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A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015a · 2015
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A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015b · 2015
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Explaining and harnessing adversarial examples
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy. 2015a · 2015
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Explaining and harnessing adversarial examples
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy. 2015b · 2015
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Jake Zhao, and Yann LeCun. 2015 · 2015
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Deepfool: A simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard. 2016 · 2016
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SGDR: stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter. 2017 · 2017
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Adversarial training methods for semi-supervised text classification
Takeru Miyato, Andrew M. Dai, and Ian J. Goodfellow. 2017 · 2017
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Towards crafting text adversarial samples
Suranjana Samanta and Sameep Mehta. 2017 · 2017
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DANCin SEQ2SEQ: Fooling text classifiers with adversarial text example generation
Catherine Wong. 2017 · 2017
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Generating natural language adversarial examples
Moustafa Alzantot, Yash Sharma, Ahmed Elgohary, Bo-Jhang Ho, Mani Srivastava, and Kai-Wei Chang. 2018 · 2018
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Training verified learners with learned verifiers
Krishnamurthy (Dj) Dvijotham, Sven Gowal, Robert Stanforth, Relja Arandjelović, Brendan O’Donoghue, Jonathan Uesato, and Pushmeet Kohli. 2018 · 2018
Cited alongside, same era.
Robust physical-world attacks on deep learning visual classification
Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei Xiao, Atul Prakash, Tadayoshi Kohno, and Dawn Song. 2018 · 2018
Cited alongside, same era.
Adversarial examples are not bugs, they are features
Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, and Aleksander Madry. 2019 · 2019
Later among the works it cites.
Certified robustness to adversarial word substitutions
Robin Jia, Aditi Raghunathan, Kerem Göksel, and Percy Liang. 2019 · 2019
Later among the works it cites.
Certified robustness to adversarial examples with differential privacy
Mathias Lécuyer, Vaggelis Atlidakis, Roxana Geambasu, Daniel Hsu, and Suman Jana. 2019 · 2019
Later among the works it cites.
Discrete adversarial attacks and submodular optimization with applications to text classification
Qi Lei, Lingfei Wu, Pin-Yu Chen, Alex Dimakis, Inderjit S. Dhillon, and Michael J. Witbrock. 2019 · 2019
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Textbugger: Generating adversarial text against real-world applications
Jinfeng Li, Shouling Ji, Tianyu Du, Bo Li, and Ting Wang. 2019 · 2019
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Black-box generation of adversarial text sequences to evade deep learning classifiers
Ji Gao, Jack Lanchantin, Mary Lou Soffa, and Yanjun Qi. 2018 · 2018
Cited alongside, same era.
On the effectiveness of interval bound propagation for training verifiably robust models
Sven Gowal, Krishnamurthy (Dj) Dvijotham, Robert Stanforth, Rudy Bunel, Chongli Qin, Jonathan Uesato, Relja Arandjelović Timothy, Timothy Mann, and Pushmeet Kohli. 2018 · 2018
Cited alongside, same era.
Adversarial examples for natural language classification problems
Volodymyr Kuleshov, Shantanu Thakoor, Tingfung Lau, and Stefano Ermon. 2018 · 2018
Cited alongside, same era.
Deep text classification can be fooled
Bin Liang, Hongcheng Li, Miaoqiang Su, Pan Bian, Xirong Li, and Wenchang Shi. 2018 · 2018
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2018 · 2018
Cited alongside, same era.
Semantically equivalent adversarial rules for debugging NLP models
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2018 · 2018
Cited alongside, same era.
Interpretable adversarial perturbation in input embedding space for text
Motoki Sato, Jun Suzuki, Hiroyuki Shindo, and Yuji Matsumoto. 2018 · 2018
Cited alongside, same era.
Ilya Loshchilov and Frank Hutter. 2019 · 2019
Later among the works it cites.
Generating adversarial examples by makeup attacks on face recognition
Zheng-An Zhu, Yun-Zhong Lu, and Chen-Kuo Chiang. 2019 · 2019
Later among the works it cites.
Seq2Sick: Evaluating the robustness of sequence-to-sequence models with adversarial examples
Minhao Cheng, Jinfeng Yi, Pin-Yu Chen, Huan Zhang, and Cho-Jui Hsieh. 2020b · 2020
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Adversarial attack and defense of structured prediction models
Wenjuan Han, Liwen Zhang, Yong Jiang, and Kewei Tu. 2020 · 2020
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Is BERT really robust? a strong baseline for natural language attack on text classification and entailment
Di Jin, Zhijing Jin, Joey Tianyi Zhou, and Peter Szolovits. 2020 · 2020
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Robustness certificates for sparse adversarial attacks by randomized ablation
Alexander Levine and Soheil Feizi. 2020 · 2020
Later among the works it cites.
BERT-ATTACK: Adversarial attack against BERT using BERT
Linyang Li, Ruotian Ma, Qipeng Guo, Xiangyang Xue, and Xipeng Qiu. 2020 · 2020
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TextAttack: A framework for adversarial attacks, data augmentation and adversarial training in NLP
John Morris, Eli Lifland, Jin Yong Yoo, Jake Grigsby, Di Jin, and Yanjun Qi. 2020 · 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 · 2020
Later among the works it cites.
Automatic perturbation analysis for scalable certified robustness and beyond
Kaidi Xu, Zhouxing Shi, Huan Zhang, Yihan Wang, Kai-Wei Chang, Minlie Huang, Bhavya Kailkhura, Xue Lin, and Cho-Jui Hsieh. 2020 · 2020
Later among the works it cites.
SAFER: A structure-free approach for certified robustness to adversarial word substitutions
Mao Ye, Chengyue Gong, and Qiang Liu. 2020 · 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 · 2020
Later among the works it cites.
Evaluating and enhancing the robustness of neural network-based dependency parsing models with adversarial examples
Xiaoqing Zheng, Jiehang Zeng, Yi Zhou, Cho-Jui Hsieh, Minhao Cheng, and Xuanjing Huang. 2020 · 2020
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FreeLB: Enhanced adversarial training for natural language understanding
Chen Zhu, Yu Cheng, Zhe Gan, Siqi Sun, Tom Goldstein, and Jingjing Liu. 2020 · 2020
Later among the works it cites.
Towards robustness against natural language word substitutions
Xinshuai Dong, Hong Liu, Rongrong Ji, and Anh Tuan Luu. 2021 · 2021
Closest in time.
Adversarial examples for evaluating reading comprehension systems
Robin Jia and Percy Liang. 2017 · 2031
Closest in time.