article · Okayama University Scientific Achievement Repository (Okayama University) · 2022
Coded DNN Watermark: Robustness against Pruning Models Using Constant Weight Code
Yasui, Tatsuya, Tanaka, Takuro, Malik, Asad, Kuribayashi, Minoru
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Abstract
Deep Neural Network (DNN) watermarking techniques are increasingly being used to protect the intellectual property of DNN models. Basically, DNN watermarking is a technique to insert side information into the DNN model without significantly degrading the performance of its original task. A pruning attack is a threat to DNN watermarking, wherein the less important neurons in the model are pruned to make it faster and more compact. As a result, removing the watermark from the DNN model is possible. This study investigates a channel coding approach to protect DNN watermarking against pruning attacks. The channel model differs completely from conventional models involving digital images. Determining the suitable encoding methods for DNN watermarking remains an open problem. Herein, we presented a novel encoding approach using constant weight codes to protect the DNN watermarking against pruning attacks. The experimental results confirmed that the robustness against pruning attacks could be controlled by carefully setting two thresholds for binary symbols in the codeword.
Cite this paper
Yasui, T., Tanaka, T., Malik, A., & Kuribayashi, M. (2022). Coded DNN Watermark: Robustness against Pruning Models Using Constant Weight Code. Okayama University Scientific Achievement Repository (Okayama University).
Yasui, Tatsuya, et al. “Coded DNN Watermark: Robustness against Pruning Models Using Constant Weight Code.” Okayama University Scientific Achievement Repository (Okayama University), 2022.
Yasui, Tatsuya, Takuro Tanaka, Asad Malik, and Minoru Kuribayashi. 2022. “Coded DNN Watermark: Robustness against Pruning Models Using Constant Weight Code.” Okayama University Scientific Achievement Repository (Okayama University).
Yasui, T. et al. (2022) ‘Coded DNN Watermark: Robustness against Pruning Models Using Constant Weight Code’, Okayama University Scientific Achievement Repository (Okayama University).
@article{yasui2022coded,
title = {{Coded DNN Watermark: Robustness against Pruning Models Using Constant Weight Code}},
author = {Yasui, Tatsuya and Tanaka, Takuro and Malik, Asad and Kuribayashi, Minoru},
journal = {Okayama University Scientific Achievement Repository (Okayama University)},
year = {2022}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.