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Recently, the research on protecting the intellectual properties (IP) of deep neural networks (DNN) has attracted serious concerns.
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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 Asia Conference on Computer and Communications Security , 2018, pp. 159–172
2018
Cited alongside, same era.
Y. Adi, C. Baum, M. Cissé, B. Pinkas, and J. Keshet, “Turning your weakness into a strength: Watermarking deep neural networks by backdooring,” in Proceedings of the 27th USENIX Security Symposium , 2018, pp. 1615–1631
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W. Jiang, “Keras-cifar10,” https://github.com/jerett/Keras-CIFAR10
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“DeepLearningDenoise,” https://github.com/shibuiwilliam/DeepLearningDenoise
2018
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Q. Zhong, L. Y. Zhang, J. Zhang, L. Gao, and Y. Xiang, “Protecting IP of deep neural networks with watermarking: A new label helps,” in Proceedings of the Advances in Knowledge Discovery and Data Mining - 24th Pacific-Asia Conference , 2020, pp. 462–474
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“WatermarkRobustness,” https://github.com/CodeSubmission642/WatermarkRobustness
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T. von Känel, “adversarial-frontier-stitching,” https://github.com/dunky11/adversarial-frontier-stitching
2021
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