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Model extraction attacks are a kind of attacks in which an adversary obtains a new model, whose performance is equivalent to that of a target model, via query access to the target model efficiently, i.e., fewer datasets and computational resources than those of the target model.
Model compression
Cristian Bucilâ, Rich Caruana, and Alexandru Niculescu-Mizil · 2006
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Differential privacy
Cynthia Dwork · 2006
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Statistics for Evidence Based Practice and Evaluation
Allen Rubin · 2012
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Practical evasion of a learning-based classifier: A case study
Nedim Ŝrndić and Pavel Laskov · 2014
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Do deep nets really need to be deep?
Jimmy Ba and Rich Caruana · 2014
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Stealing machine learning models via prediction apis
Florian Tramér, Fan Zhang, and Ari Juels · 2016
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Deep learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2016
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Learning efficient object detection models with knowledge distillation
Guobin Chen, Wongun Choi, Xiang Yu, Tony Han, and Manmohan Chandraker · 2017
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Gazoubunruishinsogakushukinitaisuru model extraction kogekinokensho
Rina Okada and Satoshi Hasegawa · 2018
Cited alongside, same era.
Model extraction warning in mlaas paradigm
Manish Kesarwani, Bhaskar Mukhoty, Vijay Arya, and Sameep Mehta · 2018
Cited alongside, same era.
Stealing hyperparameters in machine learning
Binghui Wang and Neil Zhenqiang Gong · 2018
Cited alongside, same era.
Rendered insecure: Gpu side channel attacks are practical
Hoda Naghibijouybari, Ajaya Neupane, Zhiyun Qian, and Nael Abu-Ghazaleh · 2018
Cited alongside, same era.
Csi neural network: Using side-channels to recover your artificial neural network information
Lejla Batina, Shivam Bhasin, Dirmanto Jap, and Stjepan Picek · 2018
Cited alongside, same era.
Prada: Protecting against dnn model stealing attacks
Mika Juuti, Sebastian Szyller, Samuel Marchal, and N. Asokan · 2019
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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Knockoff nets: Stealing functionality of black-box models
Tribhuvanesh Orekondy, Bernt Schiele, and Mario Fritz · 2019
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Extraction of complex dnn models: Real threat or boogeyman?
Buse Gul Atli, Sebastian Szyller, Mika Juuti, Samuel Marchal, and N. Asokan · 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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Model-extraction attack against fpga-dnn accelerator utilizing correlation electromagnetic analysis
Kota Yoshida, Takaya Kubota, Mitsuru Shiozaki, and Takeshi Fujino · 2019
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Stock market prediction using lstms
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Cited alongside, same era.
Efficiently stealing your machine learning models
Robert Nikolai Reith, Thomas Schneider, and Oleksandr Tkachenko · 2019
Cited alongside, same era.
A framework for the extraction of deep neural networks by leveraging public data
Soham Pal, Yash Gupta, Aditya Shukla, Aditya Kanade, Shirish Shevade, and Vinod Ganapathy · 2019
Cited alongside, same era.
Thieves on sesame street! model extraction of bert-based apis
Kalpesh Krishna, Singh Tomar Gaurav, P. Ankur Parikh, Nicolas Papernot, and Mohit Iyyerm · 2019
Cited alongside, same era.
Achyut Ghosh, Soumik Bose, Giridhar Maji, C. Narayan Debnath, and Soumya Sen · 2019
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DAWN: Dynamic Adversarial Watermarking of Neural Networks
Sebastian Szyller, Buse Gul Atli, Samuel Marchal, and N. Asokan · 2019
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The secret sharer: Evaluating and testing unintended memorization in neural networks
Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, and Dawn Song · 2019
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Bdpl: A boundary differentially private layer against machine learning model extraction attacks
Huadi Zheng, Qingqing Ye, Haibo Hu, Chengfang Fang, and Jie Shi · 2019
Later among the works it cites.