Fetching the paper…
Reading the bibliography…
Obtaining the state of the art performance of deep learning models imposes a high cost to model generators, due to the tedious data preparation and the substantial processing requirements.
Multimedia data-embedding and watermarking technologies
Mitchell D. Swanson, Mei Kobayashi, and Ahmed Hossam Tewfik · 1998
Earlier work this paper cites.
Watermarking digital image and video data. a state-of-the-art overview
G.C. Langelaar, Iwan Setyawan, and R.L. Lagendijk · 2000
Earlier work this paper cites.
A neural probabilistic language model
Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Janvin · 2003
Earlier work this paper cites.
Pattern Recognition and Machine Learning (Information Science and Statistics)
Christopher M. Bishop · 2006
Earlier work this paper cites.
Deep neural networks for acoustic modeling in speech recognition
Geoffrey Hinton, Li Deng, Dong Yu, George Dahl, Abdel rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara Sainath, and Brian Kingsbury · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
Earlier work this paper cites.
Speech recognition with deep recurrent neural networks
Alex Graves, Abdel-rahman Mohamed, and Geoffrey Hinton · 2013
Earlier work this paper cites.
word2vec explained: deriving mikolov et al.’s negative-sampling word-embedding method
Yoav Goldberg and Omer Levy · 2014
Earlier work this paper cites.
Deep speech: Scaling up end-to-end speech recognition
Awni Y. Hannun, Carl Case, Jared Casper, Bryan Catanzaro, Greg Diamos, Erich Elsen, Ryan Prenger, Sanjeev Satheesh, Shubho Sengupta, Adam Coates, and Andrew Y. Ng · 2014
Earlier work this paper cites.
A survey of digital watermarking techniques and its applications
Lalit Kumar Saini and Vishal Shrivastava · 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.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le · 2014
Earlier work this paper cites.
2016 data science report, Mar 2016
Crowd Flower · 2016
Earlier work this paper cites.
A primer on neural network models for natural language processing
Yoav Goldberg · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Cleaning big data: Most time-consuming, least enjoyable data science task, survey says, Mar 2016
Gil Press · 2016
Earlier work this paper cites.
Stealing machine learning models via prediction api
Florian Tramèr, Fan Zhang, Ari Juels, Michael K. Reiter, and Thomas Ristenpart · 2016
Earlier work this paper cites.
Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
Cited alongside, same era.
Targeted backdoor attacks on deep learning systems using data poisoning
Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, and Dawn Xiaodong Song · 2017
Cited alongside, same era.
Badnets: Identifying vulnerabilities in the machine learning model supply chain
Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Garg · 2017
Cited alongside, same era.
Neural trojans
Yuntao Liu, Yang Xie, and Ankur Srivastava · 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.
Fine-pruning: Defending against backdooring attacks on deep neural networks
Kang Liu, Brendan Dolan-Gavitt, and Siddharth Garg · 2018
Later among the works it cites.
Trojaning attack on neural networks
Yingqi Liu, Shiqing Ma, Yousra Aafer, Wen-Chuan Lee, Juan Zhai, Weihang Wang, and Xiangyu Zhang · 2018
Later among the works it cites.
Digital watermarking for deep neural networks
Yuki Nagai, Yusuke Uchida, Shigeyuki Sakazawa, and Shin’ichi Satoh · 2018
Later among the works it cites.
Towards reverse-engineering black-box neural networks
Seong Joon Oh, Max Augustin, Mario Fritz, and Bernt Schiele · 2018
Later among the works it cites.
Deepsigns: A generic watermarking framework for ip protection of deep learning models
Bita Darvish Rouhani, Huili Chen, and Farinaz Koushanfar · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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.
Membership inference attacks against machine learning models
R. Shokri, M. Stronati, C. Song, and V. Shmatikov · 2017
Cited alongside, same era.
Embedding watermarks into deep neural networks
Yusuke Uchida, Yuki Nagai, Shigeyuki Sakazawa, and Shin’ichi Satoh · 2017
Cited alongside, same era.
Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R Salakhutdinov, and Alexander J Smola · 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.
Deepmarks: A digital fingerprinting framework for deep neural networks
Huili Chen, Bita Darvish Rohani, and Farinaz Koushanfar · 2018
Cited alongside, same era.
Performance comparison of contemporary dnn watermarking techniques
Huili Chen, Bita Darvish Rouhani, Xinwei Fan, Osman Cihan Kilinc, and Farinaz Koushanfar · 2018
Cited alongside, same era.
Stealing hyperparameters in machine learning
B. Wang and N. Z. Gong · 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.
Blackmarks: Blackbox multibit watermarking for deep neural networks
Huili Chen, Bita Darvish Rouhani, and Farinaz Koushanfar · 2019
Closest in time.
Leveraging unlabeled data for watermark removal of deep neural networks
Xinyun Chen, Wenxiao Wang, Yiming Ding, Bender Chries, Jia Ruoxi, and Dawn Song · 2019
Closest in time.
Strip: A defence against trojan attacks on deep neural networks
Yansong Gao, Chang Xu, Derui Wang, Shiping Chen, Damith C Ranasinghe, and Surya Nepal · 2019
Closest in time.
Deepstego: Protecting intellectual property of deep neural networks by steganography
Zheng Li and Shanqing Guo · 2019
Closest in time.
Robust watermarking of neural network with exponential weighting
Ryota Namba and Jun Sakuma · 2019
Closest in time.
Knockoff nets: Stealing functionality of black-box models
Tribhuvanesh Orekondy, Bernt Schiele, and Mario Fritz · 2019
Closest in time.
Attacks on digital watermarks for deep neural networks
Florian Kerschbaum Tianhao Wang · 2019
Closest in time.
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
Closest in time.
Robust and undetectable white-box watermarks for deep neural networks, 2019
Tianhao Wang and Florian Kerschbaum · 2019
Closest in time.
Li Zhang, Chengyu Hu, Yang Zhang, and Shanqing Guo · 2019
Closest in time.