scholarly journals Multiplicative Watermarking Method with the Visual Saliency Model Using Contourlet Transform

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jinhua Liu ◽  
Jiawen Huang ◽  
Yuanyuan Huang
Keyword(s):  
Side Information ◽  
Similarity Index ◽  
Visual Saliency ◽  
Structural Similarity ◽  
High Energy ◽  
Contourlet Transform ◽  
Strength Factor ◽  
Saliency Model ◽  
Watermark Embedding ◽  
Visual Saliency Model

We have proposed an image adaptive watermarking method by using contourlet transform. Firstly, we have selected high-energy image blocks as the watermark embedding space through segmenting the original image into nonoverlapping blocks and designed a watermark embedded strength factor by taking advantage of the human visual saliency model. To achieve dynamic adjustability of the multiplicative watermark embedding parameter, the relationship between watermark embedded strength factor and watermarked image quality is developed through experiments with the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM), respectively. Secondly, to detect the watermark information, the generalized Gaussian distribution (GGD) has been utilized to model the contourlet coefficients. Furthermore, positions of the blocks selected, watermark embedding factor, and watermark size have been used as side information for watermark decoding. Finally, several experiments have been conducted on eight images, and the results prove the effectiveness of the proposed watermarking approach. Concretely, our watermarking method has good imperceptibility and strong robustness when against Gaussian noise, JPEG compression, scaling, rotation, median filtering, and Gaussian filtering attack.

scholarly journals Complex Wavelet-Based Image Watermarking with the Human Visual Saliency Model

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1462 ◽  
Author(s):  
Jinhua Liu ◽  
Yunbo Rao ◽  
Yuanyuan Huang
Keyword(s):  
Image Watermarking ◽  
Similarity Index ◽  
Low Frequency ◽  
Visual Saliency ◽  
Structural Similarity ◽  
High Entropy ◽  
Saliency Model ◽  
Watermark Embedding ◽  
Complex Wavelet ◽  
Visual Saliency Model

Imperceptibility and robustness are the two complementary, but fundamental requirements of any digital image watermarking method. To improve the invisibility and robustness of multiplicative image watermarking, a complex wavelet based watermarking algorithm is proposed by using the human visual texture masking and visual saliency model. First, image blocks with high entropy are selected as the watermark embedding space to achieve imperceptibility. Then, an adaptive multiplicative watermark embedding strength factor is designed by utilizing texture masking and visual saliency to enhance robustness. Furthermore, the complex wavelet coefficients of the low frequency sub-band are modeled by a Gaussian distribution, and a watermark decoding method is proposed based on the maximum likelihood criterion. Finally, the effectiveness of the watermarking is validated by using the peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) through experiments. Simulation results demonstrate the invisibility of the proposed method and its strong robustness against various attacks, including additive noise, image filtering, JPEG compression, amplitude scaling, rotation attack, and combinational attack.

scholarly journals Visual Saliency Model-Based Image Watermarking with Laplacian Distribution

Information ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 239
Author(s):  
Hongmei Liu ◽  
Jinhua Liu ◽  
Mingfeng Zhao
Keyword(s):  
Simulation Analysis ◽  
Image Watermarking ◽  
Additive White Gaussian Noise ◽  
Visual Saliency ◽  
High Energy ◽  
Distribution Model ◽  
Saliency Model ◽  
Detection Approach ◽  
Laplacian Distribution ◽  
Visual Saliency Model

To improve the invisibility and robustness of the multiplicative watermarking algorithm, an adaptive image watermarking algorithm is proposed based on the visual saliency model and Laplacian distribution in the wavelet domain. The algorithm designs an adaptive multiplicative watermark strength factor by utilizing the energy aggregation of the high-frequency wavelet sub-band, texture masking and visual saliency characteristics. Then, the image blocks with high-energy are selected as the watermark embedding space to implement the imperceptibility of the watermark. In terms of watermark detection, the Laplacian distribution model is used to model the wavelet coefficients, and a blind watermark detection approach is exploited based on the maximum likelihood scheme. Finally, this paper performs the simulation analysis and comparison of the performance of the proposed algorithm. Experimental results show that the proposed algorithm is robust against additive white Gaussian noise, JPEG compression, median filtering, scaling, rotation attack and other attacks.

A Novel Biologically Inspired Visual Saliency Model

2014 ◽  
Vol 6 (4) ◽  
pp. 841-848 ◽  
Author(s):  
Jingjing Zhao ◽  
Shujin Sun ◽  
Xingtong Liu ◽  
Jixiang Sun ◽  
Afeng Yang
Keyword(s):  
Visual Saliency ◽  
Biologically Inspired ◽  
Saliency Model ◽  
Visual Saliency Model

A Two-Stage Clustering Based 3D Visual Saliency Model for Dynamic Scenarios

2019 ◽  
Vol 21 (4) ◽  
pp. 809-820 ◽  
Author(s):  
You Yang ◽  
Bei Li ◽  
Pian Li ◽  
Qiong Liu
Keyword(s):  
Visual Saliency ◽  
Two Stage ◽  
Saliency Model ◽  
Visual Saliency Model

Local and Global Contrast for Saliency Estimate in DCT Domain

2013 ◽  
Vol 456 ◽  
pp. 611-615
Author(s):  
Nan Ping Ling ◽  
Han Ling Zhang
Keyword(s):  
State Of The Art ◽  
Visual Saliency ◽  
Local Contrast ◽  
Global Contrast ◽  
Contrast Method ◽  
Saliency Model ◽  
Dct Domain ◽  
Proposed Model ◽  
Roc Area ◽  
Visual Saliency Model

In this paper, we present a new bottom-up visual saliency model, which utilizes local and global contrast method to calculate the saliency in DCT domain. Our proposed method is firstly used in the DCT domain. The local contrast method uses the center-surround operation to compute the local saliency, and the global contrast method calculate the dissimilarity between DCT blocks of image and any other DCT blocks in any location. The final saliency is generated by combining the local with global contrast saliency. Experimental evaluation on a publicly available benchmark dataset shows the proposed model can acquire state-of-the-art results and outperform the other models in terms of the ROC area.

A Saliency Detection Method Based on Wavelet Transform and Simple Priors

2014 ◽  
Vol 602-605 ◽  
pp. 2238-2241
Author(s):  
Jian Kun Chen ◽  
Zhi Wei Kang
Keyword(s):  
Wavelet Transform ◽  
Detection Method ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Feature Maps ◽  
Multi Scale ◽  
Saliency Model ◽  
Proposed Model ◽  
Visual Saliency Model ◽  
Better Than

In this paper, we present a new visual saliency model, which based on Wavelet Transform and simple Priors. Firstly, we create multi-scale feature maps to represent different features from edge to texture in wavelet transform. Then we modulate local saliency at a location and its global saliency, combine the local saliency and global saliency to generate a new saliency .Finally, the final saliency is generated by combining the new saliency and two simple priors (color prior an location prior). Experimental evaluation shows the proposed model can achieve state-of-the-art results and better than the other models on a public available benchmark dataset.

Sign in / Sign up

Export Citation Format

Share Document