A novel CNN based security guaranteed image watermarking generation scenario for smart city applications

The rise of machine learning increases the current computing capabilities and paves the way to novel disruptive applications. In the current era of big data, the application of image retrieval technology for large-scale data is a popular research area. To ensure the robustness and security of digita...

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Vydáno v:Information sciences Ročník 479; s. 432 - 447
Hlavní autoři: Li, Daming, Deng, Lianbing, Bhooshan Gupta, Brij, Wang, Haoxiang, Choi, Chang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Inc 01.04.2019
Témata:
Algorithm design
Convolutional neural network
Generation scenario
Image watermark
Smart cities
Image watermark
Smart cities
Algorithm design
Convolutional neural network
Generation scenario
ISSN:0020-0255, 1872-6291
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Shrnutí:The rise of machine learning increases the current computing capabilities and paves the way to novel disruptive applications. In the current era of big data, the application of image retrieval technology for large-scale data is a popular research area. To ensure the robustness and security of digital image watermarking, we propose a novel algorithm using synergetic neural networks. The algorithm first processes a meaningful gray watermark image, then embeds it as a watermark signal into the block Discrete Cosine Transform (DCT) component. The companion algorithm for detection and extraction of the watermark uses a cooperative neural network, where the suspected watermark signal is used as the input while the output consists in the result of the recognition process. The simulation experiments show that the algorithm can complete certain image processing operations with improved performance, not only simultaneously completing watermark detection and extraction, but also efficiently determining the watermark attribution. Compared with other state-of-the-art models, the proposed model obtains an optimal Peak Signal-to-noise ratio (PSNR).
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2018.02.060