Abstract
In this paper, a novel image watermarking scheme using HVS characteristics is proposed in transform domain. The HVS is modeled using 10 rules constituting the Fuzzy Inference System (FIS) by taking into account luminance sensitivity, edge sensitivity and contrast sensitivity. The output of the FIS is a single output known as weighing factor (WF). A dataset is prepared using the image coefficients and the WF. A fast neural network known as extreme learning machine (ELM) is trained using this dataset. The ELM produces a column vector of size 1024 x 1 used as normalized watermark embedded in low frequency coefficients of gray scale images. The signed images are subject to quality assessment before and after executing image processing attacks to examine the issue of robustness vis-a-vis imperceptibility. It is concluded that the proposed Fuzzy-ELM architecture for image watermarking yields good results both for visual quality of signed / attacked images and robustness. The order of time complexity of this scheme is suitable to produce commercial watermarking applications on a real time scale.
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Mishra, A., Goel, A. (2015). A Novel HVS Based Gray Scale Image Watermarking Scheme Using Fast Fuzzy-ELM Hybrid Architecture. In: Cao, J., Mao, K., Cambria, E., Man, Z., Toh, KA. (eds) Proceedings of ELM-2014 Volume 2. Proceedings in Adaptation, Learning and Optimization, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-319-14066-7_15
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DOI: https://doi.org/10.1007/978-3-319-14066-7_15
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14065-0
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