Title
A Novel Framework Of Robust Video Watermarking Based On Statistical Model
Abstract
This paper is to investigate a novel framework of robust video watermarking based on the statistical model with robustness against multiple attacks. The main contribution is threefold. First, the Laplacian distribution is proposed to model each naive video frame, referring to as the original frame; meanwhile the noisy frame, referring to as the one with adding Gaussian-distributed noise, is modeled using the Gaussian distribution. Second, we propose a novel mechanism of embedding watermark by artificially adding noise or not, corresponding to watermark bit 1 or 0. Third, it is proposed to cast the problem of watermark extraction into the framework of hypothesis testing theory. In the ideal context, with knowing all the model parameters, the Likelihood Ratio Test (LRT) is smoothly established with verifying the feasibility of the designed watermark extraction based on the statistical models. In the case of estimating model parameters, we propose to design the Generalized Likelihood Ratio Test (GLRT) to deal with the practical problem of watermark extraction. Finally, compared with some prior arts, extensive experimental results show that our proposed novel framework of robust video watermarking can achieve the high video quality with robustness against various attacks such as re-scaling, cropping, and compression.
Year
DOI
Venue
2018
10.1007/978-3-030-00015-8_14
CLOUD COMPUTING AND SECURITY, PT IV
Keywords
Field
DocType
Robust video watermarking, Statistical model, Hypothesis testing, Parameter estimation
Digital watermarking,Likelihood-ratio test,Computer science,Algorithm,Real-time computing,Watermark,Robustness (computer science),Statistical model,Estimation theory,Video quality,Statistical hypothesis testing
Conference
Volume
ISSN
Citations 
11066
0302-9743
0
PageRank 
References 
Authors
0.34
13
6
Name
Order
Citations
PageRank
Li Li123734.83
Xin Li200.34
Tong Qiao33312.03
Xu Xiaoyu4124.61
Shan-Qing Zhang544.87
Chin Chen Chang67849725.95