A robust audio watermarking scheme based on lifting wavelet transform and singular value decomposition

2012 ◽  
Vol 92 (9) ◽  
pp. 1985-2001 ◽  
Author(s):  
Baiying Lei ◽  
Ing Yann Soon ◽  
Feng Zhou ◽  
Zhen Li ◽  
Haijun Lei
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Singular Value ◽  
Audio Watermarking ◽  
Lifting Wavelet Transform ◽  
Lifting Wavelet ◽  
Value Decomposition ◽  
Watermarking Scheme

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.

Optical image watermarking based on singular value decomposition ghost imaging and lifting wavelet transform

2019 ◽  
Vol 114 ◽  
pp. 76-82 ◽  
Author(s):  
Shiqing Wang ◽  
Xiangfeng Meng ◽  
Yongkai Yin ◽  
Yurong Wang ◽  
Xiulun Yang ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Image Watermarking ◽  
Singular Value ◽  
Optical Image ◽  
Ghost Imaging ◽  
Lifting Wavelet Transform ◽  
Lifting Wavelet ◽  
Value Decomposition
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