Convolutional Neural Network Architecture for Recovering Watermark Synchronization

In this paper, we propose a convolutional neural network-based template architecture that compensates for the disadvantages of existing watermarking techniques that are vulnerable to geometric distortion. The proposed template consists of a template generation network, a template extraction network,...

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Detalles Bibliográficos
Autores principales: Kim, Wook-Hyung, Kang, Jihyeon, Mun, Seung-Min, Hou, Jong-Uk
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7570697/
https://www.ncbi.nlm.nih.gov/pubmed/32971823
http://dx.doi.org/10.3390/s20185427

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7570697/
https://www.ncbi.nlm.nih.gov/pubmed/32971823
http://dx.doi.org/10.3390/s20185427

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