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With increasingly more data and computation involved in their training, machine learning models constitute valuable intellectual property.
Overlearning reveals sensitive attributes
Congzheng Song and Vitaly Shmatikov · 1905
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ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 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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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2012
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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One weird trick for parallelizing convolutional neural networks, 2014
Alex Krizhevsky · 2014
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Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Distilling the knowledge in a neural network, 2015
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Rethinking the inception architecture for computer vision, 2015
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna · 2015
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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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Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Łukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, and Jeffrey Dean · 2016
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Wide residual networks, 2016
Sergey Zagoruyko and Nikos Komodakis · 2016
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Understanding deep learning requires rethinking generalization, 2016
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2016
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Principled detection of out-of-distribution examples in neural networks
Shiyu Liang, Yixuan Li, and R Srikant · 2017
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Feature visualization
Chris Olah, Alexander Mordvintsev, and Ludwig Schubert · 2017
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Practical black-box attacks against machine learning, 2017
Nicolas Papernot, Patrick McDaniel, Ian 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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Embedding watermarks into deep neural networks
Yusuke Uchida, Yuki Nagai, Shigeyuki Sakazawa, and Shin’ichi Satoh · 2017
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Turning your weakness into a strength: Watermarking deep neural networks by backdooring
Yossi Adi, Carsten Baum, Moustapha Cisse, Benny Pinkas, and Joseph Keshet · 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.
Bert: Pre-training of deep bidirectional transformers for language understanding, 2018
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Cited alongside, same era.
A simple unified framework for detecting out-of-distribution samples and adversarial attacks
Kimin Lee, Kibok Lee, Honglak Lee, and Jinwoo Shin · 2018
Cited alongside, same era.
Zero-shot knowledge transfer via adversarial belief matching, 2019
Paul Micaelli and Amos Storkey · 2019
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Uniform convergence may be unable to explain generalization in deep learning
Vaishnavh Nagarajan and J. Zico Kolter · 2019
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White-box vs Black-box: Bayes Optimal Strategies for Membership Inference
Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, Yann Ollivier, and Herve Jegou · 2019
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On the robustness of the backdoor-based watermarking in deep neural networks
Masoumeh Shafieinejad, Jiaqi Wang, Nils Lukas, and Florian Kerschbaum · 2019
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Neural cleanse: Identifying and mitigating backdoor attacks in neural networks
Bolun Wang, Yuanshun Yao, Shawn Shan, Huiying Li, Bimal Viswanath, Haitao Zheng, and Ben Y Zhao · 2019
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Fine-pruning: Defending against backdooring attacks on deep neural networks
Kang Liu, Brendan Dolan-Gavitt, and Siddharth Garg · 2018
Cited alongside, same era.
The building blocks of interpretability
Chris Olah, Arvind Satyanarayan, Ian Johnson, Shan Carter, Ludwig Schubert, Katherine Ye, and Alexander Mordvintsev · 2018
Cited alongside, same era.
Knockoff nets: Stealing functionality of black-box models, 2018
Tribhuvanesh Orekondy, Bernt Schiele, and Mario Fritz · 2018
Cited alongside, same era.
The harmonic mean p-value for combining dependent tests
Daniel J. Wilson · 2018
Cited alongside, same era.
Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting
S. Yeom, I. Giacomelli, M. Fredrikson, and S. Jha · 2018
Cited alongside, same era.
Unlabeled data improves adversarial robustness
Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, Percy Liang, and John Duchi · 2019
Cited alongside, same era.
Exploring connections between active learning and model extraction, 2019
Varun Chandrasekaran, Kamalika Chaudhuri, Irene Giacomelli, Somesh Jha, and Songbai Yan · 2019
Cited alongside, same era.
Detectron2
Yuxin Wu, Alexander Kirillov, Francisco Massa, Wan-Yen Lo, and Ross Girshick · 2019
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Effectiveness of distillation attack and countermeasure on neural network watermarking, 2019
Ziqi Yang, Hung Dang, and Ee-Chien Chang · 2019
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Dreaming to distill: Data-free knowledge transfer via deepinversion, 2019
Hongxu Yin, Pavlo Molchanov, Zhizhong Li, Jose M. Alvarez, Arun Mallya, Derek Hoiem, Niraj K. Jha, and Jan Kautz · 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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Label-only membership inference attacks, 2020
Christopher A. Choquette Choo, Florian Tramer, Nicholas Carlini, and Nicolas Papernot · 2020
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High accuracy and high fidelity extraction of neural networks, 2020
Matthew Jagielski, Nicholas Carlini, David Berthelot, Alex Kurakin, and Nicolas Papernot · 2020
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Entangled watermarks as a defense against model extraction, 2020
Hengrui Jia, Christopher A. Choquette-Choo, and Nicolas Papernot · 2020
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Legal risks of adversarial machine learning research
Ram Shankar Siva Kumar, Jonathon Penney, Bruce Schneier, and Kendra Albert · 2020
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Stolen memories: Leveraging model memorization for calibrated white-box membership inference
Klas Leino and Matt Fredrikson · 2020
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OpenAI’s GPT-3 Language Model: A Technical Overview, 2020
Chuan Li · 2020
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Sponge examples: Energy-latency attacks on neural networks
Ilia Shumailov, Yiren Zhao, Daniel Bates, Nicolas Papernot, Robert Mullins, and Ross Anderson · 2020
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Data-free model extraction
Jean-Baptiste Truong, Pratyush Maini, Robert J. Walls, and Nicolas Papernot · 2021
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