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Machine-Learning-as-a-Service providers expose machine learning (ML) models through application programming interfaces (APIs) to developers.
Deep learning for anomaly detection: A survey
Raghavendra Chalapathy and Sanjay Chawla · 1901
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Prediction poisoning: Utility-constrained defenses against model stealing attacks
Tribhuvanesh Orekondy, Bernt Schiele, and Mario Fritz · 1906
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Gradient-based learning applied to document recognition
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Reading digits in natural images with unsupervised feature learning
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A kernel two-sample test
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Diederik P Kingma and Max Welling · 2014
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Adam: A method for stochastic optimization
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Explaining and harnessing adversarial examples
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Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 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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Detecting anomalous data using auto-encoders
Jerone Andrews, Edward Morton, and Lewis Griffin · 2016
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Defending against adversarial attacks by leveraging an entire GAN
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Defense-GAN: Protecting classifiers against adversarial attacks using generative models
Pouya Samangouei, Maya Kabkab, and Rama Chellappa · 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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Adversarial active learning for deep networks: a margin based approach
Melanie Ducoffe and Frédéric Precioso · 2018
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Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese · 2018
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Alexey Kurakin, Ian J. Goodfellow, and Samy Bengio · 2016
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Deepfool: A simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016
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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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MagNet: A two-pronged defense against adversarial examples
Dongyu Meng and Hao Chen · 2017
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Nicholas Carlini and David A. Wagner · 2017
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Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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Model extraction warning in MLaaS paradigm
Manish Kesarwani, Bhaskar Mukhoty, Vijay Arya, and Sameep Mehta · 2018
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Knockoff Nets: Stealing functionality of black-box models
Tribhuvanesh Orekondy, Bernt Schiele, and Mario Fritz
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Defending against neural network model stealing attacks using deceptive perturbations
T. Lee, B. Edwards, I. Molloy, and D. Su · 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
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PRADA: Protecting against DNN model stealing attacks
Mika Juuti, Sebastian Szyller, Alexey Dmitrenko, Samuel Marchal, and N. Asokan · 2019
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Extraction of complex DNN models: Real threat or boogeyman?
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ActiveThief: Model extraction using active learning and unannotated public data
Soham Pal, Yash Gupta, Aditya Shukla, Aditya Kanade, Shirish K. Shevade, and Vinod Ganapathy · 2020
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