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2018

Performance Comparison of Contemporary DNN Watermarking Techniques

Chen, Huili, Rouhani, Bita Darvish, Fan, Xinwei et al.

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DNNs shall be considered as the intellectual property (IP) of the model builder due to the impeding cost of designing/training a highly accurate model.

Research attempts have been made to protect the authorship of the trained model and prevent IP infringement using DNN watermarking techniques.
In this paper, we provide a comprehensive performance comparison of the state-of-the-art DNN watermarking methodologies according to the essential requisites for an effective watermarking technique.
We identify the pros and cons of each scheme and provide insights into the underlying rationale.

Performance Comparison of Contemporary DNN Watermarking Techniques · Around

Built on

M. Ribeiro, K. Grolinger, and M. A. Capretz, “Mlaas: Machine learning as a service,” in IEEE 14th International Conference on Machine Learning and Applications (ICMLA) , 2015
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Earlier work this paper cites.
E. L. Merrer, P. Perez, and G. Trédan, “Adversarial frontier stitching for remote neural network watermarking,” arXiv preprint arXiv:1711.01894 , 2017
Original
2017
Earlier work this paper cites.
Y. Uchida et al. , “Embedding watermarks into deep neural networks,” https://github.com/yu4u/dnn-watermark, 2017
2017
Earlier work this paper cites.

Similar

Y. Uchida, Y. Nagai, S. Sakazawa, and S. Satoh, “Embedding watermarks into deep neural networks,” in Proceedings of the ACM on International Conference on Multimedia Retrieval , 2017
2017
Cited alongside, same era.
Y. Adi, C. Baum, M. Cisse, B. Pinkas, and J. Keshet, “Turning your weakness into a strength: Watermarking deep neural networks by backdooring,” Usenix Security Symposium , 2018
2018
Cited alongside, same era.

Then

B. D. Rouhani, H. Chen, and F. Koushanfar, “Deepsigns: A generic watermarking framework for ip protection of deep learning models,” arXiv preprint arXiv:1804.00750 , 2018
Original
2018
Closest in time.
J. Zhang, Z. Gu, J. Jang, H. Wu, M. P. Stoecklin, H. Huang, and I. Molloy, “Protecting intellectual property of deep neural networks with watermarking,” in Proceedings of the 2018 on Asia Conference on Computer and Communications Security . ACM, 2018, pp. 159–172
Original
2018
Closest in time.

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