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The Universal Trigger (UniTrigger) is a recently-proposed powerful adversarial textual attack method.
Using honeypots to catch adversarial attacks on neural networks
Shawn Shan, Emily Wenger, Bolun Wang, Bo Li, Haitao Zheng, and Ben Y Zhao. 2019 · 1904
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Is bert really robust? natural language attack on text classification and entailment
Di Jin, Zhijing Jin, Joey Tianyi Zhou, and Peter Szolovits. 2019 · 1907
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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. 2019b · 1907
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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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Natural language adversarial attacks and defenses in word level
Xiaosen Wang, Hao Jin, and Kun He. 2019c · 1909
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Technique of clear writing
Robert Gunning et al. 1952 · 1952
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Bae: Bert-based adversarial examples for text classification
Siddhant Garg and Goutham Ramakrishnan. 2020 · 2004
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Streamlining tensor and network pruning in pytorch
Michela Paganini and Jessica Forde. 2020 · 2004
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A sentimental education: Sentiment analysis using subjectivity
Bo Pang and Lillian Lee. 2004 · 2004
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Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
Bo Pang and Lillian Lee. 2005 · 2005
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Chuanshuai Chen and Jiazhu Dai. 2020 · 2007
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A partial break of the honeypots defense to catch adversarial attacks
Nicholas Carlini. 2020 · 2009
Cited alongside, same era.
Onion: A simple and effective defense against textual backdoor attacks
Fanchao Qi, Yangyi Chen, Mukai Li, Zhiyuan Liu, and Maosong Sun. 2020 · 2011
Cited alongside, same era.
Convolutional neural networks for sentence classification
Yoon Kim. 2014 · 2014
Cited alongside, same era.
Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015 · 2015
Cited alongside, same era.
Practical black-box attacks against machine learning
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami. 2017 · 2017
Cited alongside, same era.
Towards robust detection of adversarial examples
Tianyu Pang, Chao Du, Yinpeng Dong, and Jun Zhu. 2018 · 2018
Later among the works it cites.
Understanding measures of uncertainty for adversarial example detection
Lewis Smith and Yarin Gal. 2018 · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Improving the robustness of question answering systems to question paraphrasing
Wee Chung Gan and Hwee Tou Ng. 2019 · 2019
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Abs: Scanning neural networks for back-doors by artificial brain stimulation
Yingqi Liu, Wen-Chuan Lee, Guanhong Tao, Shiqing Ma, Yousra Aafer, and Xiangyu Zhang. 2019a · 2019
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Daniel Cer, Yinfei Yang, Sheng-yi Kong, Nan Hua, Nicole Limtiaco, Rhomni St John, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, et al. 2018 · 2018
Cited alongside, same era.
Hotflip: White-box adversarial examples for text classification
Javid Ebrahimi, Anyi Rao, Daniel Lowd, and Dejing Dou. 2018 · 2018
Cited alongside, same era.
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.
TextBugger: Generating Adversarial Text Against Real-world Applications
Jinfeng Li, Shouling Ji, Tianyu Du, Bo Li, and Ting Wang. 2018 · 2018
Cited alongside, same era.
Fine-pruning: Defending against backdooring attacks on deep neural networks
Kang Liu, Brendan Dolan-Gavitt, and Siddharth Garg. 2018 · 2018
Cited alongside, same era.
Characterizing adversarial subspaces using local intrinsic dimensionality
Xingjun Ma, Bo Li, Yisen Wang, Sarah M Erfani, Sudanthi Wijewickrema, Grant Schoenebeck, Dawn Song, Michael E Houle, and James Bailey. 2018 · 2018
Cited alongside, same era.
GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. 2019a
Cited in the paper.
Combating adversarial misspellings with robust word recognition
Danish Pruthi, Bhuwan Dhingra, and Zachary C Lipton. 2019 · 2019
Later among the works it cites.
Defending neural backdoors via generative distribution modeling
Ximing Qiao, Yukun Yang, and Hai Li. 2019 · 2019
Later among the works it cites.
Neural cleanse: Identifying and mitigating backdoor attacks in neural networks
Bolun Wang, Yuanshun Yao, Shawn Shan, Huiying Li, Bimal Viswanath, Haitao Zheng, and Ben Y Zhao. 2019b · 2019
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Malcom: Generating malicious comments to attack neural fake news detection models
Thai Le, Suhang Wang, and Dongwon Lee. 2020 · 2020
Closest in time.
Minimum uncertainty based detection of adversaries in deep neural networks
Fatemeh Sheikholeslami, Swayambhoo Jain, and Georgios B Giannakis. 2020 · 2020
Closest in time.