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Textual adversarial samples play important roles in multiple subfields of NLP research, including security, evaluation, explainability, and data augmentation.
Spam filtering with naive bayes-which naive bayes?
Vangelis Metsis, Ion Androutsopoulos, and Georgios Paliouras. 2006 · 2006
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Detecting privacy leaks using corpus-based association rules
Richard Chow, Philippe Golle, and Jessica Staddon. 2008 · 2008
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Email spam filtering: A systematic review
Gordon V Cormack et al. 2008 · 2008
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Learning to attack: Towards textual adversarial attacking in real-world situations
Yuan Zang, Bairu Hou, Fanchao Qi, Zhiyuan Liu, Xiaojun Meng, and Maosong Sun. 2020a · 2009
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Overview of the trec 2010 legal track
Gordon V Cormack, Maura R Grossman, Bruce Hedin, and Douglas W Oard. 2010 · 2010
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Learning task experiments in the trec 2010 legal track
Stephen Tomlinson. 2010 · 2010
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Fake review detection: Classification and analysis of real and pseudo reviews
Arjun Mukherjee, Vivek Venkataraman, Bing Liu, Natalie Glance, et al. 2013 · 2013
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Redacting sensitive information in software artifacts
Mark Grechanik, Collin McMillan, Tathagata Dasgupta, Denys Poshyvanyk, and Malcom Gethers. 2014 · 2014
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Semi-automated text classification for sensitivity identification
Giacomo Berardi, Andrea Esuli, Craig Macdonald, Iadh Ounis, and Fabrizio Sebastiani. 2015 · 2015
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Florin: a system to support (near) real-time applications on user generated content on daily news
Qingyuan Liu, Eduard C Dragut, Arjun Mukherjee, and Weiyi Meng. 2015 · 2015
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Image-based recommendations on styles and substitutes
Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton Van Den Hengel. 2015 · 2015
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Measuring, characterizing, and avoiding spam traffic costs
Osvaldo Fonseca, Elverton Fazzion, Italo Cunha, Pedro Henrique Bragioni Las-Casas, Dorgival Guedes, Wagner Meira, Cristine Hoepers, Klaus Steding-Jessen, and Marcelo HP Chaves. 2016 · 2016
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Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering
Ruining He and Julian McAuley. 2016 · 2016
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Seeking nonsense, looking for trouble: Efficient promotional-infection detection through semantic inconsistency search
Xiaojing Liao, Kan Yuan, XiaoFeng Wang, Zhongyu Pei, Hao Yang, Jianjun Chen, Haixin Duan, Kun Du, Eihal Alowaisheq, Sumayah Alrwais, et al. 2016 · 2016
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Crafting adversarial input sequences for recurrent neural networks
Nicolas Papernot, Patrick McDaniel, Ananthram Swami, and Richard Harang. 2016 · 2016
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Exploiting product related review features for fake review detection
Chengai Sun, Qiaolin Du, and Gang Tian. 2016 · 2016
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Deep learning for hate speech detection in tweets
Pinkesh Badjatiya, Shashank Gupta, Manish Gupta, and Vasudeva Varma. 2017 · 2017
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Automated hate speech detection and the problem of offensive language
Thomas Davidson, Dana Warmsley, Michael Macy, and Ingmar Weber. 2017 · 2017
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Hotflip: White-box adversarial examples for text classification
Javid Ebrahimi, Anyi Rao, Daniel Lowd, and Dejing Dou. 2017 · 2017
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Adversarial examples for evaluating reading comprehension systems
Robin Jia and Percy Liang. 2017 · 2017
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Truth of varying shades: Analyzing language in fake news and political fact-checking
Hannah Rashkin, Eunsol Choi, Jin Yea Jang, Svitlana Volkova, and Yejin Choi. 2017 · 2017
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Towards crafting text adversarial samples
Suranjana Samanta and Sameep Mehta. 2017 · 2017
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Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. 2017 · 2017
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Satirical news detection and analysis using attention mechanism and linguistic features
Fan Yang, Arjun Mukherjee, and Eduard Dragut. 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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Overview of the evalita 2018 hate speech detection task
Cristina Bosco, Dell’Orletta Felice, Fabio Poletto, Manuela Sanguinetti, and Tesconi Maurizio. 2018 · 2018
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On adversarial examples for character-level neural machine translation
Javid Ebrahimi, 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.
Adversarial example generation with syntactically controlled paraphrase networks
Mohit Iyyer, John Wieting, Kevin Gimpel, and Luke Zettlemoyer. 2018 · 2018
Cited alongside, same era.
A survey on open information extraction
Christina Niklaus, Matthias Cetto, André Freitas, and Siegfried Handschuh. 2018 · 2018
Cited alongside, same era.
A survey on fake review detection using machine learning techniques
Nidhi A Patel and Rakesh Patel. 2018 · 2018
Cited alongside, same era.
Beyond accuracy: Behavioral testing of NLP models with CheckList
Marco Tulio Ribeiro, Tongshuang Wu, Carlos Guestrin, and Sameer Singh. 2020 · 2020
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Toxic comment detection in online discussions
Julian Risch and Ralf Krestel. 2020 · 2020
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Mind your inflections! improving nlp for non-standard englishes with base-inflection encoding
Samson Tan, Shafiq Joty, Lav R Varshney, and Min-Yen Kan. 2020 · 2020
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Into the deep web: Understanding e-commercefraud from autonomous chat with cybercriminals
Peng Wang Wang, Xiaojing Liao Liao, Yue Qin, and XiaoFeng Wang. 2020b · 2020
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SAFER: A structure-free approach for certified robustness to adversarial word substitutions
Mao Ye, Chengyue Gong, and Qiang Liu. 2020 · 2020
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An overview of online fake news: Characterization, detection, and discussion
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Hate speech on twitter: A pragmatic approach to collect hateful and offensive expressions and perform hate speech detection
Hajime Watanabe, Mondher Bouazizi, and Tomoaki Ohtsuki. 2018 · 2018
Cited alongside, same era.
Reading thieves’ cant: automatically identifying and understanding dark jargons from cybercrime marketplaces
Kan Yuan, Haoran Lu, Xiaojing Liao, and XiaoFeng Wang. 2018 · 2018
Cited alongside, same era.
Deep learning for sentiment analysis: A survey
Lei Zhang, Shuai Wang, and Bing Liu. 2018 · 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 · 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 · 2019
Cited alongside, same era.
Certified robustness to adversarial word substitutions
Robin Jia, Aditi Raghunathan, Kerem Göksel, and Percy Liang. 2019 · 2019
Cited alongside, same era.
Training on synthetic noise improves robustness to natural noise in machine translation
Vladimir Karpukhin, Omer Levy, Jacob Eisenstein, and Marjan Ghazvininejad. 2019 · 2019
Cited alongside, same era.
Xichen Zhang and Ali A Ghorbani. 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
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Types of out-of-distribution texts and how to detect them
Udit Arora, William Huang, and He He. 2021 · 2021
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Defending pre-trained language models from adversarial word substitution without performance sacrifice
Rongzhou Bao, Jiayi Wang, and Hai Zhao. 2021 · 2021
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Robustness gym: Unifying the NLP evaluation landscape
Karan Goel, Nazneen Fatema Rajani, Jesse Vig, Zachary Taschdjian, Mohit Bansal, and Christopher Ré. 2021 · 2021
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Pre-trained models: Past, present and future
Xu Han, Zhengyan Zhang, Ning Ding, Yuxian Gu, Xiao Liu, Yuqi Huo, Jiezhong Qiu, Yuan Yao, Ao Zhang, Liang Zhang, et al. 2021 · 2021
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Achieving model robustness through discrete adversarial training
Maor Ivgi and Jonathan Berant. 2021 · 2021
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Contextualized perturbation for textual adversarial attack
Dianqi Li, Yizhe Zhang, Hao Peng, Liqun Chen, Chris Brockett, Ming-Ting Sun, and Bill Dolan. 2021 · 2021
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Generating natural language attacks in a hard label black box setting
Rishabh Maheshwary, Saket Maheshwary, and Vikram Pudi. 2021 · 2021
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Exploring data and model poisoning attacks to deep learning-based nlp systems
Fiammetta Marulli, Laura Verde, and Lelio Campanile. 2021 · 2021
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Frequency-guided word substitutions for detecting textual adversarial examples
Maximilian Mozes, Pontus Stenetorp, Bennett Kleinberg, and Lewis Griffin. 2021 · 2021
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Mind the style of text! adversarial and backdoor attacks based on text style transfer
Fanchao Qi, Yangyi Chen, Xurui Zhang, Mukai Li, Zhiyuan Liu, and Maosong Sun. 2021 · 2021
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Better robustness by more coverage: Adversarial and mixup data augmentation for robust finetuning
Chenglei Si, Zhengyan Zhang, Fanchao Qi, Zhiyuan Liu, Yasheng Wang, Qun Liu, and Maosong Sun. 2021 · 2021
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Concealed data poisoning attacks on NLP models
Eric Wallace, Tony Zhao, Shi Feng, and Sameer Singh. 2021 · 2021
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Bridge the gap between cv and nlp! a gradient-based textual adversarial attack framework
Lifan Yuan, Yichi Zhang, Yangyi Chen, and Wei Wei. 2021 · 2021
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OpenAttack: An open-source textual adversarial attack toolkit
Guoyang Zeng, Fanchao Qi, Qianrui Zhou, Tingji Zhang, Zixian Ma, Bairu Hou, Yuan Zang, Zhiyuan Liu, and Maosong Sun. 2021 · 2021
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Defense against synonym substitution-based adversarial attacks via Dirichlet neighborhood ensemble
Yi Zhou, Xiaoqing Zheng, Cho-Jui Hsieh, Kai-Wei Chang, and Xuanjing Huang. 2021 · 2021
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A unified evaluation of textual backdoor learning: Frameworks and benchmarks
Ganqu Cui, Lifan Yuan, Bingxiang He, Yangyi Chen, Zhiyuan Liu, and Maosong Sun. 2022 · 2022
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Creating and detecting fake reviews of online products
Joni Salminen, Chandrashekhar Kandpal, Ahmed Mohamed Kamel, Soon gyo Jung, and Bernard J. Jansen. 2022 · 2022
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Exploring the universal vulnerability of prompt-based learning paradigm
Lei Xu, Yangyi Chen, Ganqu Cui, Hongcheng Gao, and Zhiyuan Liu. 2022 · 2022
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