Abstract
Rapid development and application of big data technology is speeding up the dissemination and utilization of visible media big data. The intensive application of visual media brings about some security issues. The building of copyright protection mechanism of visual media big data under Internet environment can provide safety guarantee for content services of visual media in a digital library, and enhance integrated serviceability of digital resources in the digital library. Firstly, sparse features are extracted by exploiting the data sparsity and perception sparsity of visual media, and then a sparse perception computing model for visual media big data is built. Then the design idea of visual media copyright encryption, copyright authentication, copyright notice, and audit trail scheme are given from the point of view of sparse feature computation of visual media. Finally, a copyright protection application scheme of visual media big data under the network environment is developed. Its application in the digital library validates the effectiveness of this copyright protection mechanism of visual media. It can safeguard the confidentiality, integrity, availability, and auditing of visual media big data in the digital library, and enhance content serviceability of visual media in the digital library under big data environment.
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Acknowledgements
This work was supported in component by the National Natural Science Foundation of China under Grant No. 61872408, the Natural Science Foundation of Hunan Province (2020JJ4238), the Social Science Fund of Hunan Province under Grant No. 16YBA102, and the Research Fund of Hunan Provincial Key Laboratory of informationization technology for basic education under Grant No. 2015TP1017.
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Wang, S., Qin, J., Jiang, M., Gao, Z. (2021). A Copyright Protection Scheme of Visual Media in Big Data Environment. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2021. Lecture Notes in Computer Science(), vol 12736. Springer, Cham. https://doi.org/10.1007/978-3-030-78609-0_53
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