A hybrid semi-blind digital image watermarking technique using lifting wavelet transform — Singular value decomposition

2011 ◽  
Author(s):  
Meenakshi S Arya ◽  
Rajesh Siddavatam ◽  
S P Ghrera
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Digital Image ◽  
Image Watermarking ◽  
Singular Value ◽  
Digital Image Watermarking ◽  
Lifting Wavelet Transform ◽  
Lifting Wavelet ◽  
Value Decomposition

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.

LWT-DCT based Image Watermarking Scheme using Normalized SVD

2020 ◽  
Vol 13 ◽  
Author(s):  
Rahul Dixit ◽  
Amita Nandal ◽  
Arvind Dhaka ◽  
Vardan Agarwal ◽  
Yohan Varghese
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Discrete Cosine Transform ◽  
Image Watermarking ◽  
Singular Value ◽  
Cover Image ◽  
Lifting Wavelet Transform ◽  
Cosine Transform ◽  
Lifting Wavelet ◽  
Value Decomposition

Background: Nowadays information security is one of the biggest issues of social networks. The multimedia data can be tampered with, and the attackers can then claim its ownership. Image watermarking is a technique that is used for copyright protection and authentication of multimedia. Objective: We aim to create a new and more robust image watermarking technique to prevent illegal copying, editing and distribution of media. Method : The watermarking technique proposed in this paper is non-blind and employs Lifting Wavelet Transform on the cover image to decompose the image into four coefficient matrices. Then Discrete Cosine Transform is applied which separates a selected coefficient matrix into different frequencies and later Singular Value Decomposition is applied. Singular Value Decomposition is also applied to the watermarking image and it is added to the singular matrix of the cover image which is then normalized followed by the inverse Singular Value Decomposition, inverse Discrete Cosine Transform and inverse Lifting Wavelet Transform respectively to obtain an embedded image. Normalization is proposed as an alternative to the traditional scaling factor. Results: Our technique is tested against attacks like rotation, resizing, cropping, noise addition and filtering. The performance comparison is evaluated based on Peak Signal to Noise Ratio, Structural Similarity Index Measure, and Normalized Cross-Correlation. Conclusion: The experimental results prove that the proposed method performs better than other state-of-the-art techniques and can be used to protect multimedia ownership.

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