An adaptive framework to image watermarking based on the twin support vector regression and genetic algorithm in lifting wavelet transform domain

2020 ◽  
Vol 79 (25-26) ◽  
pp. 18657-18678
Author(s):  
Rajesh Mehta ◽  
Keshav Gupta ◽  
Ashok Kumar Yadav
Keyword(s):  
Genetic Algorithm ◽  
Wavelet Transform ◽  
Support Vector Regression ◽  
Image Watermarking ◽  
Support Vector ◽  
Transform Domain ◽  
Lifting Wavelet Transform ◽  
Twin Support Vector Regression ◽  
Lifting Wavelet ◽  
Adaptive Framework

Image Watermarking Technology Based on Lifting Wavelet and Fuzzy Cluster

2013 ◽  
Vol 462-463 ◽  
pp. 284-287
Author(s):  
Yan Qi ◽  
Xing Qiao Wang
Keyword(s):  
Wavelet Transform ◽  
Image Watermarking ◽  
Chaotic Map ◽  
Low Frequency ◽  
Fuzzy Cluster ◽  
Transform Domain ◽  
Lifting Wavelet Transform ◽  
Fuzzy Cluster Analysis ◽  
Texture Region ◽  
Lifting Wavelet

A self-adaptive blind watermarking algorithm based on transform domain was proposed by combining lifting wavelet transform with fuzzy cluster analysis. Lifting wavelet transform were used to obtain the low frequency subgraph efficiently. In low frequency subgraph, FCM and HVS were combined to divide the texture region for embedding watermark. And the watermark image was encrypted by chaotic map. Contrastive attacking experiments show that this algorithm has preferable transparency and robustness, and can resist various types of attacks effectively.

Lagrangian twin support vector regression and genetic algorithm based robust grayscale image watermarking

2016 ◽  
Vol 75 (15) ◽  
pp. 9371-9394 ◽  
Author(s):  
Ashok Kumar Yadav ◽  
Rajesh Mehta ◽  
Raj Kumar ◽  
Virendra P. Vishwakarma
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Regression ◽  
Image Watermarking ◽  
Support Vector ◽  
Grayscale Image ◽  
Twin Support Vector Regression

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.

Image enhancement in lifting wavelet transform domain

2019 ◽  
Author(s):  
Anuj Bhardwaj ◽  
Anjali Wadhwa ◽  
Vivek Singh Verma
Keyword(s):  
Wavelet Transform ◽  
Image Enhancement ◽  
Transform Domain ◽  
Lifting Wavelet Transform ◽  
Lifting Wavelet

Robust and sparse Dual Tree Complex Wavelet Transform-based Twin Support Vector regression for dense 5G InH  Communications

2021 ◽  
Author(s):  
anis charrada ◽  
Abdelaziz Samet
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Support Vector Regression ◽  
Path Loss ◽  
Series Data ◽  
Support Vector ◽  
Sensitivity Threshold ◽  
Discrete Wavelet ◽  
Twin Support Vector Regression ◽  
Receiver Sensitivity

Abstract A robust and sparse Twin Support Vector Regression based on Dual Tree Discrete Wavelet Transform algorithm is conceived in this paper and applied to 28, 38, 60 and 73-GHz LOS (Line-of-Sight) wireless multipath transmission system in 5G Indoor Hotspot (InH) settings (simple, semi-complex and complex conference rooms) under small receiver sensitivity threshold. The algorithm establishes a denoising process in the learning phase based on Dual Tree Discrete Wavelet Transform (DT-CWT) which is suitable for time-series data. Additionally, the Close-In (CI) free space reference distance path loss model is analyzed and the large-scale propagation and probability distribution functions are investigated by determining the PLE (Path Loss Exponent) and the standard deviation of Shadow Factor (SF) for each InH scenario under consideration. Performance are evaluated under twelve (12) configuration scenarios, according to three criteria: mobility (0/3mps), receiver sensitivity threshold (-80/-120 dBm) and type of the InH area (simple, semi-complex and complex conference room). Experimental results confirm the effectiveness of the proposed approach compared to other standard techniques.

Sign in / Sign up

Export Citation Format

Share Document