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Model Stealing (MS) attacks allow an adversary with black-box access to a Machine Learning model to replicate its functionality, compromising the confidentiality of the model.
Introduction to optimization. optimization software
Boris T Polyak · 1987
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Catastrophic interference in connectionist networks: The sequential learning problem
Michael McCloskey and Neal J Cohen · 1989
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Adversarial learning
Daniel Lowd and Christopher Meek · 2005
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The unreasonable effectiveness of data
Alon Halevy, Peter Norvig, and Fernando Pereira · 2009
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Stochastic first-and zeroth-order methods for nonconvex stochastic programming
Saeed Ghadimi and Guanghui Lan · 2013
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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 · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Optimal rates for zero-order convex optimization: The power of two function evaluations
John C Duchi, Michael I Jordan, Martin J Wainwright, and Andre Wibisono · 2015
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Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
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Explaining and harnessing adversarial examples
Ian Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Sergey Zagoruyko and Nikos Komodakis · 2016
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Sergey Zagoruyko and Nikos Komodakis · 2016
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Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
Cited alongside, same era.
Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
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Sijia Liu, Jie Chen, Pin-Yu Chen, and Alfred O Hero · 2017
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Random gradient-free minimization of convex functions
Yurii Nesterov and Vladimir Spokoiny · 2017
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Practical black-box attacks against machine learning
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami · 2017
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Privacy risk in machine learning: Analyzing the connection to overfitting
Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha · 2018
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Boundary attack++: Query-efficient decision-based adversarial attack
Jianbo Chen and Michael I Jordan · 2019
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Data-free adversarial distillation
Gongfan Fang, Jie Song, Chengchao Shen, Xinchao Wang, Da Chen, and Mingli Song · 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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Defending against model stealing attacks with adaptive misinformation, 2019
Sanjay Kariyappa and Moinuddin K Qureshi · 2019
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Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
Cited alongside, same era.
The space of transferable adversarial examples
Florian Tramèr, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel · 2017
Cited alongside, same era.
Query-efficient hard-label black-box attack: An optimization-based approach
Minhao Cheng, Thong Le, Pin-Yu Chen, Jinfeng Yi, Huan Zhang, and Cho-Jui Hsieh · 2018
Cited alongside, same era.
Black-box adversarial attacks with limited queries and information
Andrew Ilyas, Logan Engstrom, Anish Athalye, and Jessy Lin · 2018
Cited alongside, same era.
Defending against model stealing attacks using deceptive perturbations
Taesung Lee, Benjamin Edwards, Ian Molloy, and Dong Su · 2018
Cited alongside, same era.
Milad Nasr, Reza Shokri, and Amir Houmansadr · 2018
Cited alongside, same era.
Stealing hyperparameters in machine learning
Binghui Wang and Neil Zhenqiang Gong · 2018
Cited alongside, same era.
Zero-shot knowledge transfer via adversarial belief matching
Paul Micaelli and Amos J Storkey · 2019
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Towards reverse-engineering black-box neural networks
Seong Joon Oh, Bernt Schiele, and Mario Fritz · 2019
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Knockoff nets: Stealing functionality of black-box models
Tribhuvanesh Orekondy, Bernt Schiele, and Mario Fritz · 2019
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Model weight theft with just noise inputs: The curious case of the petulant attacker
Nicholas Roberts, Vinay Uday Prabhu, and Matthew McAteer · 2019
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Dreaming to distill: Data-free knowledge transfer via deepinversion
Hongxu Yin, Pavlo Molchanov, Zhizhong Li, Jose M Alvarez, Arun Mallya, Derek Hoiem, Niraj K Jha, and Jan Kautz · 2019
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The secret revealer: Generative model-inversion attacks against deep neural networks
Yuheng Zhang, Ruoxi Jia, Hengzhi Pei, Wenxiao Wang, Bo Li, and Dawn Song · 2019
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Prediction poisoning: Towards defenses against dnn model stealing attacks
Tribhuvanesh Orekondy, Bernt Schiele, and Mario Fritz · 2020
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