Fetching the paper…
Reading the bibliography…
In Machine Learning as a Service, a provider trains a deep neural network and gives many users access.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
Earlier work this paper cites.
Densenet: Implementing efficient convnet descriptor pyramids
Forrest Iandola, Matt Moskewicz, Sergey Karayev, Ross Girshick, Trevor Darrell, and Kurt Keutzer · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
Earlier work this paper cites.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Earlier work this paper cites.
Identity mappings in deep residual networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
Earlier work this paper cites.
Delving into transferable adversarial examples and black-box attacks
Yanpei Liu, Xinyun Chen, Chang Liu, and Dawn Song · 2016
Earlier work this paper cites.
Transferability in machine learning: from phenomena to black-box attacks using adversarial samples
Nicolas Papernot, Patrick McDaniel, and Ian Goodfellow · 2016
Earlier work this paper cites.
Stealing machine learning models via prediction apis
Florian Tramèr, Fan Zhang, Ari Juels, Michael K Reiter, and Thomas Ristenpart · 2016
Earlier work this paper cites.
Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
Cited alongside, same era.
A downsampled variant of imagenet as an alternative to the cifar datasets
Patryk Chrabaszcz, Ilya Loshchilov, and Frank Hutter · 2017
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
Cited alongside, same era.
Adversarial frontier stitching for remote neural network watermarking
Erwan Le Merrer, Patrick Perez, and Gilles Trédan · 2017
Cited alongside, same era.
Practical black-box attacks against machine learning
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami · 2017
Cited alongside, same era.
Recent trends in deep learning based natural language processing
Tom Young, Devamanyu Hazarika, Soujanya Poria, and Erik Cambria · 2018
Later among the works it cites.
Protecting intellectual property of deep neural networks with watermarking
Jialong Zhang, Zhongshu Gu, Jiyong Jang, Hui Wu, Marc Ph Stoecklin, Heqing Huang, and Ian Molloy · 2018
Later among the works it cites.
Xiaoyu Cao, Jinyuan Jia, and Neil Zhenqiang Gong · 2019
Closest in time.
A guide to deep learning in healthcare
Andre Esteva, Alexandre Robicquet, Bharath Ramsundar, Volodymyr Kuleshov, Mark DePristo, Katherine Chou, Claire Cui, Greg Corrado, Sebastian Thrun, and Jeff Dean · 2019
Closest in time.
Evasion attacks against watermarking techniques found in mlaas systems
Dorjan Hitaj, Briland Hitaj, and Luigi V Mancini · 2019
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Embedding watermarks into deep neural networks
Yusuke Uchida, Yuki Nagai, Shigeyuki Sakazawa, and Shin’ichi Satoh · 2017
Cited alongside, same era.
To prune, or not to prune: exploring the efficacy of pruning for model compression
Michael Zhu and Suyog Gupta · 2017
Cited alongside, same era.
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.
Cinic-10 is not imagenet or cifar-10
Luke N Darlow, Elliot J Crowley, Antreas Antoniou, and Amos J Storkey · 2018
Cited alongside, same era.
Boosting adversarial attacks with momentum
Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, and Jianguo Li · 2018
Cited alongside, same era.
Have you stolen my model? evasion attacks against deep neural network watermarking techniques
Dorjan Hitaj and Luigi V Mancini · 2018
Cited alongside, same era.
Adversarial robustness toolbox v0. 4.0
Maria-Irina Nicolae, Mathieu Sinn, Minh Ngoc Tran, Ambrish Rawat, Martin Wistuba, Valentina Zantedeschi, Nathalie Baracaldo, Bryant Chen, Heiko Ludwig, Ian M Molloy, et al · 2018
Cited alongside, same era.
Matthew Jagielski, Nicholas Carlini, David Berthelot, Alex Kurakin, and Nicolas Papernot · 2019
Closest in time.
Thieves on sesame street! model extraction of bert-based apis
Kalpesh Krishna, Gaurav Singh Tomar, Ankur P Parikh, Nicolas Papernot, and Mohit Iyyer · 2019
Closest in time.
Knockoff nets: Stealing functionality of black-box models
Tribhuvanesh Orekondy, Bernt Schiele, and Mario Fritz · 2019
Closest in time.
On the robustness of the backdoor-based watermarking in deep neural networks
Masoumeh Shafieinejad, Jiaqi Wang, Nils Lukas, and Florian Kerschbaum · 2019
Closest in time.
Dawn: Dynamic adversarial watermarking of neural networks
Sebastian Szyller, Buse Gul Atli, Samuel Marchal, and N Asokan · 2019
Closest in time.
Universal, transferable and targeted adversarial attacks
Junde Wu and Rao Fu · 2019
Closest in time.
Tensorflow privacy
Galen Andrew, Steve Chien, and Nicolas Papernot · 2020
Closest in time.
Cleaning big data: Most time-consuming, least enjoyable data science task, survey says
Gil Press · 2020
Closest in time.