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Machine learning (ML) models are applied in an increasing variety of domains.
Blackmarks: Blackbox multibit watermarking for deep neural networks
H. Chen, B. D. Rouhani, and F. Koushanfar · 1904
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Piracy resistant watermarks for deep neural networks
H. Li, E. Wenger, B. Y. Zhao, and H. Zheng · 1910
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Learning a nonlinear embedding by preserving class neighbourhood structure
R. Salakhutdinov and Geoff Hinton · 2007
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Mnist handwritten digit database, 2010
Y. LeCun, Corinna Cortes, and C. J. Burges · 2010
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G. Ateniese, G. Felici, L. V. Mancini, A. Spognardi, A. Villani, and D. Vitali · 2013
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Pruning algorithms of neural networks—a comparative study
M. Augasta and T. Kathirvalavakumar · 2013
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Speeding up convolutional neural networks with low rank expansions
M. Jaderberg, A. Vedaldi, and A. Zisserman · 2014
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Learning and transferring mid-level image representations using convolutional neural networks
M. Oquab, L. Bottou, I. Laptev, and J. Sivic · 2014
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A survey of digital watermarking techniques and its applications
L. K. Saini and V. Shrivastava · 2014
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Cnn features off-the-shelf: an astounding baseline for recognition
A. Sharif Razavian, H. Azizpour, J. Sullivan, and S. Carlsson · 2014
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Compressing neural networks with the hashing trick
W. Chen, J. Wilson, S. Tyree, K. Weinberger, and Y. Chen · 2015
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Distilling the knowledge in a neural network
G. Hinton, O. Vinyals, and J. Dean · 2015
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Eie: Efficient inference engine on compressed deep neural network
S. Han, X. Liu, H. Mao, J. Pu, A. Pedram, M. A. Horowitz, and W. J. Dally · 2016
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Distillation as a defense to adversarial perturbations against deep neural networks
N. Papernot, P. McDaniel, X. Wu, S. Jha, and A. Swami · 2016
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Convolutional neural networks for medical image analysis: Full training or fine tuning?
N. Tajbakhsh, J. Y. Shin, S. R. Gurudu, R. T. Hurst, C. B. Kendall, M. B. Gotway, and J. Liang · 2016
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Stealing machine learning models via prediction apis
F. Tramèr, Fan Zhang, Ari Juels, Michael K. Reiter, and Thomas Ristenpart · 2016
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Machine learning models that remember too much
C. Song, T. Ristenpart, and V. Shmatikov · 2017
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Embedding watermarks into deep neural networks
Y. Uchida, Y. Nagai, S. Sakazawa, and S. Satoh · 2017
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Turning your weakness into a strength: Watermarking deep neural networks by backdooring
Y. Adi, Carsten Baum, Moustapha Cisse, Benny Pinkas, and Joseph Keshet · 2018
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Csi neural network: Using side-channels to recover your artificial neural network information
L. Batina, S. Bhasin, D. Jap, and S. Picek · 2018
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Protect your deep neural networks from piracy
M. Chen and M. Wu · 2018
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Watermarking deep neural networks for embedded systems
J. Guo and M. Potkonjak · 2018
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A survey on methods and theories of quantized neural networks
Y. Guo · 2018
Cited alongside, same era.
Have you stolen my model? evasion attacks against deep neural network watermarking techniques
D. Hitaj and L. V. Mancini · 2018
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Model extraction warning in mlaas paradigm
M. Kesarwani, B. Mukhoty, V. Arya, and S. Mehta · 2018
Cited alongside, same era.
Fine-pruning: Defending against backdooring attacks on deep neural networks
Visual decoding of hidden watermark in trained deep neural network
S. Sakazawa, E. Myodo, K. Tasaka, and H. Yanagihara · 2019
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Mimosanet: An unrobust neural network preventing model stealing
K. Szentannai, J. Al-Afandi, and A. Horváth · 2019
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Neural cleanse: Identifying and mitigating backdoor attacks in neural networks
B. Wang, Y. Yao, S. Shan, H. Li, B. Viswanath, H. Zheng, and B. Y. Zhao · 2019
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Robust and undetectable white-box watermarks for deep neural networks
T. Wang and F. Kerschbaum · 2019
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A novel method for identifying the deep neural network model with the serial number
X. Xu, Y. Li, and C. Yuan · 2019
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K. Liu, B. Dolan-Gavitt, and S. Garg · 2018
Cited alongside, same era.
Sok: Security and privacy in machine learning
N. Papernot, P. McDaniel, A. Sinha, and M. P. Wellman · 2018
Cited alongside, same era.
Deepsecure: Scalable provably-secure deep learning
B. D. Rouhani, M. S. Riazi, and F. Koushanfar · 2018
Cited alongside, same era.
Attacks on digital watermarks for deep neural networks
T. Wang and F. Kerschbaum · 2018
Cited alongside, same era.
I know what you see: Power side-channel attack on convolutional neural network accelerators
L. Wei, B. Luo, Y. Li, Y. Liu, and Q. Xu · 2018
Cited alongside, same era.
Protecting intellectual property of deep neural networks with watermarking
J. Zhang, Z. Gu, J. Jang, H. Wu, M. P. Stoecklin, H. Huang, and I. Molloy · 2018
Cited alongside, same era.
Sequential triggers for watermarking of deep reinforcement learning policies
V. Behzadan and W. Hsu · 2019
Cited alongside, same era.
Later among the works it cites.
Effectiveness of distillation attack and countermeasure on neural network watermarking
Z. Yang, H. Dang, and E.-C. Chang · 2019
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Afa: Adversarial fingerprinting authentication for deep neural networks
J. Zhao, Q. Hu, G. Liu, X. Ma, F. Chen, and M. M. Hassan · 2019
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Cryptanalytic extraction of neural network models
N. Carlini, M. Jagielski, and I. Mironov · 2020
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High accuracy and high fidelity extraction of neural networks
M. Jagielski, N. Carlini, D. Berthelot, A. Kurakin, and N. Papernot · 2020
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Protect, show, attend and tell: Image captioning model with ownership protection
J. H. Lim, C. S. Chan, K. W. Ng, L. Fan, and Q. Yang · 2020
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Watermarking in deep neural networks via error back-propagation
J. Wang, H. Wu, X. Zhang, and Y. Yao · 2020
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Dnn intellectual property protection: Taxonomy, methods, attack resistance, and evaluations
M. Xue, C. He, J. Wang, and W. Liu · 2020
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Neural network laundering: Removing black-box backdoor watermarks from deep neural networks
W. Aiken, H. Kim, S. Woo, and J. Ryoo · 2021
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Refit: a unified watermark removal framework for deep learning systems with limited data
X. Chen, W. Wang, C. Bender, Y. Ding, R. Jia, B. Li, and D. Song · 2021
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Entangled watermarks as a defense against model extraction
H. Jia, C. A. Choquette-Choo, V. Chandrasekaran, and N. Papernot · 2021
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A survey of deep neural network watermarking techniques
Y. Li, H. Wang, and M. Barni · 2021
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Removing backdoor-based watermarks in neural networks with limited data
X. Liu, F. Li, B. Wen, and Q. Li · 2021
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Protecting intellectual property of generative adversarial networks from ambiguity attacks
D. S. Ong, C. S. Chan, K. W. Ng, L. Fan, and Q. Yang · 2021
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On the robustness of backdoor-based watermarking in deep neural networks
M. Shafieinejad, N. Lukas, J. Wang, X. Li, and F. Kerschbaum · 2021
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