Fetching the paper…
Reading the bibliography…
Given a state-of-the-art deep neural network text classifier, we show the existence of a universal and very small perturbation vector (in the embedding space) that causes natural text to be misclassified with high probability.
Generating textual adversarial examples for deep learning models: A survey
Wei Emma Zhang, Quan Z. Sheng, and Ahoud Abdulrahmn F. Alhazmi. 2019 · 1901
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. 2013 · 2013
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014 · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
The limitations of deep learning in adversarial settings
Nicolas Papernot, Patrick D. McDaniel, Somesh Jha, Matt Fredrikson, Z. Berkay Celik, and Ananthram Swami. 2015 · 2015
Earlier work this paper cites.
Adversarial perturbations against deep neural networks for malware classification
Kathrin Grosse, Nicolas Papernot, Praveen Manoharan, Michael Backes, and Patrick D. McDaniel. 2016 · 2016
Earlier work this paper cites.
Deepfool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard. 2016 · 2016
Earlier work this paper cites.
Crafting adversarial input sequences for recurrent neural networks
Nicolas Papernot, Patrick McDaniel, Ananthram Swami, and Richard Harang. 2016 · 2016
Earlier work this paper cites.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. 2016 · 2016
Cited alongside, same era.
Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner. 2017 · 2017
Cited alongside, same era.
Houdini: Fooling deep structured prediction models
Moustapha Cisse, Yossi Adi, Natalia Neverova, and Joseph Keshet. 2017 · 2017
Cited alongside, same era.
Hotflip: White-box adversarial examples for text classification
Javid Ebrahimi, Anyi Rao, Daniel Lowd, and Dejing Dou. 2017 · 2017
Cited alongside, same era.
Adversarial examples for evaluating reading comprehension systems
Robin Jia and Percy Liang. 2017 · 2017
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Later among the works it cites.
Textbugger: Generating adversarial text against real-world applications
Jinfeng Li, Shouling Ji, Tianyu Du, Bo Li, and Ting Wang. 2018 · 2018
Later among the works it cites.
Semantically equivalent adversarial rules for debugging nlp models
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2018 · 2018
Later among the works it cites.
Generating adversarial text samples
Suranjana Samanta and Sameep Mehta. 2018 · 2018
Later among the works it cites.
Universal adversarial training
Ali Shafahi, Mahyar Najibi, Zheng Xu, John Dickerson, Larry S Davis, and Tom Goldstein. 2018 · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Deep text classification can be fooled
Bin Liang, Hongcheng Li, Miaoqiang Su, Pan Bian, Xirong Li, and Wenchang Shi. 2017 · 2017
Cited alongside, same era.
Fast feature fool: A data independent approach to universal adversarial perturbations
Konda Reddy Mopuri, Utsav Garg, and R. Venkatesh Babu. 2017 · 2017
Cited alongside, same era.
Generative adversarial perturbations
Omid Poursaeed, Isay Katsman, Bicheng Gao, and Serge J. Belongie. 2017 · 2017
Cited alongside, same era.
Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, and Samy Bengio. 2016a
Cited in the paper.
Adversarial machine learning at scale
Alexey Kurakin, Ian Goodfellow, and Samy Bengio. 2016b
Cited in the paper.
Universal adversarial perturbations
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, and Pascal Frossard. 2017a
Cited in the paper.
Analysis of universal adversarial perturbations
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, Pascal Frossard, and Stefano Soatto. 2017b
Cited in the paper.
Identify susceptible locations in medical records via adversarial attacks on deep predictive models
Mengying Sun, Fengyi Tang, Jinfeng Yi, Fei Wang, and Jiayu Zhou. 2018 · 2018
Later among the works it cites.
Glue: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amapreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman. 2018 · 2018
Later among the works it cites.
Universal adversarial attacks on text classifiers
Melika Behjati, Seyed-Mohsen Moosavi-Dezfooli, Mahdieh Soleymani Baghshah, and Pascal Frossard. 2019 · 2019
Closest in time.