Tree-structured Bayesian compressive sensing based image watermarking

Xiumei Li; Huang Bai; Junmei Sun

doi:10.1117/12.2557243

31 December 2019 Tree-structured Bayesian compressive sensing based image watermarking

Xiumei Li, Huang Bai, Junmei Sun

Proceedings Volume 11384, Eleventh International Conference on Signal Processing Systems; 1138412 (2019) https://doi.org/10.1117/12.2557243
Event: Eleventh International Conference on Signal Processing Systems, 2019, Chengdu, China

Abstract

Watermarking is of vital importance for copyright protection and content authentication of images. With the development of compressive sensing, it has been successfully applied for watermarking with improved performance. Since an image can exhibit tree structure in wavelet domain, a new watermarking embedding and extraction method is proposed based on tree-structured Bayesian compressive sensing. The Markov Chain Monte Carlo (MCMC) method and the variational Bayesian (VB) analysis can be used for inference, respectively. Attacks to the watermarking, such as Gaussian noise, salt and pepper noise, Gaussian filtering, and JPEG compression, are given to evaluate the watermarking robustness with comparison to other reported reconstruction algorithms such as basis pursuit, orthogonal matching pursuit, Bayesian compressive sensing using relevance vector machine (RVM), and Bayesian compressive sensing with VB. Simulation results and comparisons show remarkable advantages of the tree-structured Bayesian compressive sensing for watermarking embedding and extraction.

Citation Download Citation

Xiumei Li, Huang Bai, and Junmei Sun "Tree-structured Bayesian compressive sensing based image watermarking", Proc. SPIE 11384, Eleventh International Conference on Signal Processing Systems, 1138412 (31 December 2019); https://doi.org/10.1117/12.2557243

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available