Robust method of digital image watermarking using SVD transform on DWT coefficients with optimal block

Radouane, Mohamed; Messoussi, Rochdi; Touahni, Raja; Boujiha, Tarik

doi:10.1109/icmcs.2014.6911190

Cited by 6 publications

(1 citation statement)

References 12 publications

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“…Radouane et al, [5] proposed a robust watermarking method for copyright protection. The proposed method is primarily based on the combination of three transformations DWT, SVD and DCT using optimal block ensure the various properties of digital image watermarking like imperceptibility, capacity and robustness.…”

Section: Literature Reviewmentioning

confidence: 99%

An Improvement in Digital Image Watermarking Scheme Based on Singular Value Decomposition and Wavelet Transform

Saqib¹,

Naaz²

2019

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An enormous growth of multimedia information in the internet has given rise to varied unauthorized use and modification. This authenticity issue is equally a limitation both in the defense data transmission and secured transmission. Digital watermarking is one of the legitimate solutions to the above problem since it makes possible validation and secure transmission of secret data. This paper presents a robust and secure digital image watermarking scheme that can be used for copyright protection. The scheme involves Lifting Wavelet Transform (LWT) and Singular Value Decomposition (SVD). The latest approach to wavelet transform is the Lifting Wavelet Transform and the significant transform technique for robust digital image watermarking is Singular Value Decomposition. The results are cross-validated by using inverse LWT and SVD. The digital signature mechanism is used to generate and embed a digital signature after the watermark is embedded, then the ownership is authenticated before extracting watermarks. In order to check the robustness of the method against various common image processing attacks like Mean, Median, Gaussian, Shear, Rotation and Crop, certain performance metrics such as peak to signal noise ratio (PSNR)and mean square error (MSE) is computed. To check similarity, normal cross-correlation (NCC) is used.

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Section: Literature Reviewmentioning

confidence: 99%