A batch copyright scheme for digital image based on deep neural network
Digital signature and watermarking are effective image copyright protection techniques. However, these methods come with some inherent drawbacks, including the incapacity of carrying information and inevitable fidelity loss, respectively. To improve this situation, this paper proposes a neural netwo...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
AIMS Press
2019-07-01
|
| Series: | Mathematical Biosciences and Engineering |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2019306?viewType=HTML |
| _version_ | 1811233896592834560 |
|---|---|
| author | Haoyu Lu Daofu Gong Fenlin Liu Hui Liu Jinghua Qu |
| author_facet | Haoyu Lu Daofu Gong Fenlin Liu Hui Liu Jinghua Qu |
| author_sort | Haoyu Lu |
| collection | DOAJ |
| description | Digital signature and watermarking are effective image copyright protection techniques. However, these methods come with some inherent drawbacks, including the incapacity of carrying information and inevitable fidelity loss, respectively. To improve this situation, this paper proposes a neural network-based image batch copyright protection scheme, with which a copyright message bitstream can be extracted from each registered image while no modifications are introduced. Taking advantage of the pattern extraction capability and the error tolerance of the neural network, the proposed scheme achieves perfect imperceptibility and superior robustness. Moreover, the network's preference for diverse data content makes it especially appropriate for multiple images copyright verification. These claims will be further supported by the experimental results in this paper. |
| first_indexed | 2024-04-12T11:26:49Z |
| format | Article |
| id | doaj.art-b09abe58133c4aec9f502b0e2374993d |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-04-12T11:26:49Z |
| publishDate | 2019-07-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-b09abe58133c4aec9f502b0e2374993d2022-12-22T03:35:12ZengAIMS PressMathematical Biosciences and Engineering1551-00182019-07-011656121613310.3934/mbe.2019306A batch copyright scheme for digital image based on deep neural networkHaoyu Lu0Daofu Gong1Fenlin Liu2Hui Liu 3Jinghua Qu 41. Zhengzhou Science and Technology Institute, Zhengzhou, 450001, China1. Zhengzhou Science and Technology Institute, Zhengzhou, 450001, China1. Zhengzhou Science and Technology Institute, Zhengzhou, 450001, China2. University of Surrey, Guildford, Surrey, GU2 7XH, UK1. Zhengzhou Science and Technology Institute, Zhengzhou, 450001, ChinaDigital signature and watermarking are effective image copyright protection techniques. However, these methods come with some inherent drawbacks, including the incapacity of carrying information and inevitable fidelity loss, respectively. To improve this situation, this paper proposes a neural network-based image batch copyright protection scheme, with which a copyright message bitstream can be extracted from each registered image while no modifications are introduced. Taking advantage of the pattern extraction capability and the error tolerance of the neural network, the proposed scheme achieves perfect imperceptibility and superior robustness. Moreover, the network's preference for diverse data content makes it especially appropriate for multiple images copyright verification. These claims will be further supported by the experimental results in this paper.https://www.aimspress.com/article/doi/10.3934/mbe.2019306?viewType=HTMLdigital imagecopyright protectiondeep neural networkrobust feature extractiondigital watermarking |
| spellingShingle | Haoyu Lu Daofu Gong Fenlin Liu Hui Liu Jinghua Qu A batch copyright scheme for digital image based on deep neural network Mathematical Biosciences and Engineering digital image copyright protection deep neural network robust feature extraction digital watermarking |
| title | A batch copyright scheme for digital image based on deep neural network |
| title_full | A batch copyright scheme for digital image based on deep neural network |
| title_fullStr | A batch copyright scheme for digital image based on deep neural network |
| title_full_unstemmed | A batch copyright scheme for digital image based on deep neural network |
| title_short | A batch copyright scheme for digital image based on deep neural network |
| title_sort | batch copyright scheme for digital image based on deep neural network |
| topic | digital image copyright protection deep neural network robust feature extraction digital watermarking |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2019306?viewType=HTML |
| work_keys_str_mv | AT haoyulu abatchcopyrightschemefordigitalimagebasedondeepneuralnetwork AT daofugong abatchcopyrightschemefordigitalimagebasedondeepneuralnetwork AT fenlinliu abatchcopyrightschemefordigitalimagebasedondeepneuralnetwork AT huiliu abatchcopyrightschemefordigitalimagebasedondeepneuralnetwork AT jinghuaqu abatchcopyrightschemefordigitalimagebasedondeepneuralnetwork AT haoyulu batchcopyrightschemefordigitalimagebasedondeepneuralnetwork AT daofugong batchcopyrightschemefordigitalimagebasedondeepneuralnetwork AT fenlinliu batchcopyrightschemefordigitalimagebasedondeepneuralnetwork AT huiliu batchcopyrightschemefordigitalimagebasedondeepneuralnetwork AT jinghuaqu batchcopyrightschemefordigitalimagebasedondeepneuralnetwork |