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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Bibliographic Details
Published in:Information sciences Vol. 479; pp. 432 - 447
Main Authors: Li, Daming, Deng, Lianbing, Bhooshan Gupta, Brij, Wang, Haoxiang, Choi, Chang
Format: Journal Article
Language:English
Published: Elsevier Inc 01.04.2019
Subjects:
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
Online Access:Get full text
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Summary: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