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Multipurpose watermarking approach for copyright and integrity of steganographic autoencoder models
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Gu, Wei, Chang, Ching-Chun, Bai, Yu, Fan, Yunyuan, Tao, Liang and Li, Li (2021) Multipurpose watermarking approach for copyright and integrity of steganographic autoencoder models. Security and Communication Networks, 2021 . 9936661. doi:10.1155/2021/9936661 ISSN 1939-0122.
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Official URL: https://doi.org/10.1155/2021/9936661
Abstract
With the great achievements of deep learning technology, neural network models have emerged as a new type of intellectual property. Neural network models’ design and training require considerable computational resources and time. Watermarking is a potential solution for achieving copyright protection and integrity of neural network models without excessively compromising the models’ accuracy and stability. In this work, we develop a multipurpose watermarking method for securing the copyright and integrity of a steganographic autoencoder referred to as “HiDDen.” This autoencoder model is used to hide different kinds of watermark messages in digital images. Copyright information is embedded with imperceptibly modified model parameters, and integrity is verified by embedding the Hash value generated from the model parameters. Experimental results show that the proposed multipurpose watermarking method can reliably identify copyright ownership and localize tampered parts of the model parameters. Furthermore, the accuracy and robustness of the autoencoder model are perfectly preserved.
Item Type: | Journal Article | ||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
SWORD Depositor: | Library Publications Router | ||||||
Journal or Publication Title: | Security and Communication Networks | ||||||
Publisher: | Hindawi | ||||||
ISSN: | 1939-0122 | ||||||
Official Date: | 30 December 2021 | ||||||
Dates: |
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Volume: | 2021 | ||||||
Article Number: | 9936661 | ||||||
DOI: | 10.1155/2021/9936661 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||
Date of first compliant deposit: | 11 March 2022 | ||||||
Date of first compliant Open Access: | 11 March 2022 | ||||||
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