Non-decimated wavelet domain based robust blind digital image watermarking scheme using singular value decomposition

2016 ◽  
Author(s):  
Nagarjuna Venkat P ◽  
Lala Bhaskar ◽  
Ramachandra Reddy B
Keyword(s):  
Singular Value Decomposition ◽  
Digital Image ◽  
Image Watermarking ◽  
Singular Value ◽  
Digital Image Watermarking ◽  
Wavelet Domain ◽  
Value Decomposition ◽  
Watermarking Scheme

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

2019 ◽  
Vol 8 (1) ◽  
pp. 62-68
Author(s):  
Manasha Saqib ◽  
Sameena Naaz
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Digital Image ◽  
Digital Signature ◽  
Image Watermarking ◽  
Singular Value ◽  
Digital Image Watermarking ◽  
Lifting Wavelet Transform ◽  
Value Decomposition ◽  
Watermarking Scheme

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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