Multiplicative Watermarking Method with the Visual Saliency Model Using Contourlet Transform

Jinhua Liu,Yuanyuan Huang,Jiawen Huang

doi:10.1155/2021/1325573

Abstract

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.

Highlights

Transmitting and sharing digital multimedia have become more convenient with the rapid development of the network
Inspired by literature [28], an image watermarking algorithm was developed based on the visual saliency model in the contourlet transform domain. e main contributions of our work are summarized as follows: (1) An adaptive watermark embedded strength factor is exploited with a visual saliency model, which can achieve a good trade-off between the robustness and imperceptibility of the watermarking
We have developed an image watermarking algorithm by using the visual saliency model in the contourlet domain

Summary

Introduction

Transmitting and sharing digital multimedia have become more convenient with the rapid development of the network. A local optimal detection model that is suitable for any host signal was derived by conducting hypothesis testing analysis in this domain These methods [9, 10] can detect watermark information effectively, their parameter estimation process is complex. To further enhance the robustness of quantization watermarking, some researchers have designed corresponding quantization watermarking algorithms by combining the just noticeable distortion (JND) model, image texture complexity, and texture direction features, such as texture direction quantization [22], pair quantization based on extended JND [23, 24], and mixed modulation quantization using singular value decomposition [25]. Inspired by literature [28], an image watermarking algorithm was developed based on the visual saliency model in the contourlet transform domain. (1) An adaptive watermark embedded strength factor is exploited with a visual saliency model, which can achieve a good trade-off between the robustness and imperceptibility of the watermarking.

Brief Introduction of Contourlet Transform

Watermark Embedding and Decoding

Experimental Results

10 Image Peppers Man

Conclusion