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Current model extraction attacks assume that the adversary has access to a surrogate dataset with characteristics similar to the proprietary data used to train the victim model.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Adversarial learning
Daniel Lowd and Christopher Meek · 2005
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80 million tiny images: A large data set for nonparametric object and scene recognition
Antonio Torralba, Rob Fergus, and William T. Freeman · 2008
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L. Li, Kai Li, and Li Fei-Fei · 2009
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The unreasonable effectiveness of data
Alon Halevy, Peter Norvig, and Fernando Pereira · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y. Ng · 2011
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Finite sample convergence rates of zero-order stochastic optimization methods
Andre Wibisono, Martin J Wainwright, Michael Jordan, and John C Duchi · 2012
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Do deep nets really need to be deep?
Jimmy Ba and Rich Caruana · 2014
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Generative adversarial networks, 2014
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Fitnets: Hints for thin deep nets, 2015
Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio · 2015
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Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
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Towards principled methods for training generative adversarial networks, 2017
Martin Arjovsky and Léon Bottou · 2017
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Wasserstein gan, 2017
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models
Pin-Yu Chen, Huan Zhang, Yash Sharma, Jinfeng Yi, and Cho-Jui Hsieh · 2017
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Data-free knowledge distillation for deep neural networks, 2017
Raphael Gontijo Lopes, Stefano Fenu, and Thad Starner · 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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Transfer Learning - Machine Learning’s Next Frontier
Sebastian Ruder · 2017
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Cyclical learning rates for training neural networks, 2017
Leslie N. Smith · 2017
On the efficacy of knowledge distillation, 2019
Jang Hyun Cho and Bharath Hariharan · 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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Zero-shot knowledge transfer via adversarial belief matching
Paul Micaelli and Amos J Storkey · 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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A framework for the extraction of deep neural networks by leveraging public data, 2019
Soham Pal, Yash Gupta, Aditya Shukla, Aditya Kanade, Shirish Shevade, and Vinod Ganapathy · 2019
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Energy and policy considerations for deep learning in nlp, 2019
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Cited alongside, same era.
Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer, 2017
Sergey Zagoruyko and Nikos Komodakis · 2017
Cited alongside, same era.
Deep mutual learning, 2017
Ying Zhang, Tao Xiang, Timothy M. Hospedales, and Huchuan Lu · 2017
Cited alongside, same era.
Practical black-box attacks on deep neural networks using efficient query mechanisms
Arjun Nitin Bhagoji, Warren He, Bo Li, and Dawn Song · 2018
Cited alongside, same era.
Model extraction and active learning
V. Chandrasekaran, K. Chaudhuri, I. Giacomelli, S. Jha, and Songbai Yan · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Knowledge distillation by on-the-fly native ensemble, 2018
Xu Lan, Xiatian Zhu, and Shaogang Gong · 2018
Cited alongside, same era.
Emma Strubell, Ananya Ganesh, and Andrew McCallum · 2019
Later among the works it cites.
Autozoom: Autoencoder-based zeroth order optimization method for attacking black-box neural networks
Chun-Chen Tu, Paishun Ting, Pin-Yu Chen, Sijia Liu, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh, and Shin-Ming Cheng · 2019
Later among the works it cites.
Be your own teacher: Improve the performance of convolutional neural networks via self distillation, 2019
Linfeng Zhang, Jiebo Song, Anni Gao, Jingwei Chen, Chenglong Bao, and Kaisheng Ma · 2019
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Language models are few-shot learners, 2020
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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Data-free network quantization with adversarial knowledge distillation
Yoojin Choi, Jihwan Choi, Mostafa El-Khamy, and Jungwon Lee · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale, 2020
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2020
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Knowledge distillation: A survey, 2020
Jianping Gou, Baosheng Yu, Stephen John Maybank, and Dacheng Tao · 2020
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Maze: Data-free model stealing attack using zeroth-order gradient estimation, 2020
Sanjay Kariyappa, Atul Prakash, and Moinuddin Qureshi · 2020
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A primer on zeroth-order optimization in signal processing and machine learning, 2020
Sijia Liu, Pin-Yu Chen, Bhavya Kailkhura, Gaoyuan Zhang, Alfred Hero, and Pramod K. Varshney · 2020
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Sponge examples: Energy-latency attacks on neural networks, 2020
Ilia Shumailov, Yiren Zhao, Daniel Bates, Nicolas Papernot, Robert Mullins, and Ross Anderson · 2020
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