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We study the problem of model extraction in natural language processing, in which an adversary with only query access to a victim model attempts to reconstruct a local copy of that model.
Prediction poisoning: Utility-constrained defenses against model stealing attacks
Tribhuvanesh Orekondy, Bernt Schiele, and Mario Fritz · 1906
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Switchboard: Telephone speech corpus for research and development
John J Godfrey, Edward C Holliman, and Jane McDaniel · 1992
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Adversarial learning
Daniel Lowd and Christopher Meek · 2005
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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, Andrew Ng, and Christopher Potts · 2013
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The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning · 2014
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 2016
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Stealing machine learning models via prediction apis
Florian Tramèr, Fan Zhang, Ari Juels, Michael K Reiter, and Thomas Ristenpart · 2016
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Automatically evading classifiers
Weilin Xu, Yanjun Qi, and David Evans · 2016
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Simple and scalable predictive uncertainty estimation using deep ensembles
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Pointer sentinel mixture models
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher · 2017
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Practical black-box attacks against machine learning
Nicolas Papernot, Patrick D. McDaniel, Ian J. Goodfellow, Somesh Jha, Z. Berkay Celik, and Ananthram Swami · 2017
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Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2017
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Synthetic and natural noise both break neural machine translation
Yonatan Belinkov and Yonatan Bisk · 2018
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Model extraction and active learning
Varun Chandrasekaran, Kamalika Chaudhuri, Irene Giacomelli, Somesh Jha, and Songbai Yan · 2018
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Copycat cnn: Stealing knowledge by persuading confession with random non-labeled data
Jacson Rodrigues Correia-Silva, Rodrigo F Berriel, Claudine Badue, Alberto F de Souza, and Thiago Oliveira-Santos · 2018
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Hotflip: White-box adversarial examples for text classification
Javid Ebrahimi, Anyi Rao, Daniel Lowd, and Dejing Dou · 2018
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Pathologies of neural models make interpretations difficult
Shi Feng, Eric Wallace, Alvin Grissom II, Mohit Iyyer, Pedro Rodriguez, and Jordan Boyd-Graber · 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
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Membership inference attacks on sequence-to-sequence models
Sorami Hisamoto, Matt Post, and Kevin Duh · 2019
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Adversarial examples are not bugs, they are features
Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, and Aleksander Madry · 2019
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High-fidelity extraction of neural network models
Matthew Jagielski, Nicholas Carlini, David Berthelot, Alex Kurakin, and Nicolas Papernot · 2019
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Prada: protecting against dnn model stealing attacks
Mika Juuti, Sebastian Szyller, Samuel Marchal, and N Asokan · 2019
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Allennlp: A deep semantic natural language processing platform
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Few sample knowledge distillation for efficient network compression
Tianhong Li, Jianguo Li, Zhuang Liu, and Changshui Zhang · 2018
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Machine Learning with Membership Privacy using Adversarial Regularization
Milad Nasr, Reza Shokri, and Amir Houmansadr · 2018
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Deep k-nearest neighbors: Towards confident, interpretable and robust deep learning
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Deep contextualized word representations
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel R Bowman · 2018
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Zero-shot knowledge transfer via adversarial belief matching
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Model reconstruction from model explanations
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Zero-shot knowledge distillation in deep networks
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A framework for the extraction of deep neural networks by leveraging public data
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Model weight theft with just noise inputs: The curious case of the petulant attacker
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Dawn: Dynamic adversarial watermarking of neural networks
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Learning and evaluating general linguistic intelligence
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