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A well-trained DNN model can be regarded as an intellectual property (IP) of the model owner.
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B. D. Rouhani, H. Chen, and F. Koushanfar, “DeepSigns: An end-to-end watermarking framework for ownership protection of deep neural networks,” in Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems , 2019, pp. 485–497
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H. Chen, C. Fu, B. D. Rouhani, J. Zhao, and F. Koushanfar, “DeepAttest: An end-to-end attestation framework for deep neural networks,” in Proceedings of the 46th International Symposium on Computer Architecture , 2019, pp. 487–498
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N. Pittaras, F. Markatopoulou, V. Mezaris, and I. Patras, “Comparison of fine-tuning and extension strategies for deep convolutional neural networks,” in Proceedings of the International Conference on Multimedia Modeling , vol. 10132, 2017, pp. 102–114
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M. Chen and M. Wu, “Protect your deep neural networks from piracy,” in Proceedings of the IEEE International Workshop on Information Forensics and Security , 2018, pp. 1–7
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L. Fan, K. Ng, and C. S. Chan, “Rethinking deep neural network ownership verification: Embedding passports to defeat ambiguity attacks,” in Proceedings of the Annual Conference on Neural Information Processing Systems , 2019, pp. 4716–4725
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E. Le Merrer, P. Perez, and G. Trédan, “Adversarial frontier stitching for remote neural network watermarking,” Neural Computing and Applications , vol. 32, no. 13, pp. 9233–9244, 2020
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