基于 DWT-SVD 的混合自适应图像水印方案

Q3 Computer Science
Sachin Gaur, Navneet Tripathi, Jyoti Pandey
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引用次数: 0

摘要

在数字时代,保护数字文档的所有权和数据真实性是一项重大挑战。为了解决数字媒体的版权保护和数据验证问题,数字水印作为一种解决方案应运而生。在本文中,我们希望通过提出我们提出的结合离散小波变换(DWT)和奇异值分解(SVD)的自适应混合图像水印方法,为这个新兴和令人兴奋的领域做出适度的贡献。我们的方法包括对主图像和水印应用DWT,然后对两幅图像的Low-Low (LL)分量使用SVD进行奇异分解。然后通过水印的奇异值对主机图像的奇异值进行修改,然后进行反奇异值分解,再进行反小波变换,得到水印图像。然后,应用反向过程获得水印图像。最后,我们通过测量原始图像和水印图像之间的峰值信噪比(PSNR)以及原始图像和提取的水印之间的归一化互相关(NCC)来评估我们的方法的性能。仿真结果表明,该方法在鲁棒性、不可感知性和容量等方面都优于已有的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Hybrid DWT-SVD Based Adaptive Image Watermarking Scheme
In the digital age, protecting the ownership and data veracity of digital documents is a major challenge. To address the issues concerning copyright protection and data verification of digital media, digital watermarking has emerged as a solution. In this paper, we aspire to make a modest contribution to this emerging and exciting field by presenting our proposed adaptive hybrid image watermarking approach that combines Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD). Our method involves applying DWT to both the host image and watermark, followed by singular decomposition using SVD on the Low-Low (LL) component of both images. Now modify the singular values of the host image by the singular values of the watermark, and then inverse SVD is applied, followed by inverse DWT, to obtain the watermarked image. After that, the reverse process is applied to obtain the watermark image. Finally, we evaluate our approach’s performance by measuring the Peak Signal-to-Noise Ratio (PSNR) between the original and watermarked image as well as the Normalized Cross-Correlation (NCC) between the original and extracted watermark. Simulation results indicate that the proposed method is rich in terms of robustness, imperceptibility and capacity than the previously presented schemes.
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来源期刊
中国图象图形学报
中国图象图形学报 Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
1.20
自引率
0.00%
发文量
6776
期刊介绍: Journal of Image and Graphics (ISSN 1006-8961, CN 11-3758/TB, CODEN ZTTXFZ) is an authoritative academic journal supervised by the Chinese Academy of Sciences and co-sponsored by the Institute of Space and Astronautical Information Innovation of the Chinese Academy of Sciences (ISIAS), the Chinese Society of Image and Graphics (CSIG), and the Beijing Institute of Applied Physics and Computational Mathematics (BIAPM). The journal integrates high-tech theories, technical methods and industrialisation of applied research results in computer image graphics, and mainly publishes innovative and high-level scientific research papers on basic and applied research in image graphics science and its closely related fields. The form of papers includes reviews, technical reports, project progress, academic news, new technology reviews, new product introduction and industrialisation research. The content covers a wide range of fields such as image analysis and recognition, image understanding and computer vision, computer graphics, virtual reality and augmented reality, system simulation, animation, etc., and theme columns are opened according to the research hotspots and cutting-edge topics. Journal of Image and Graphics reaches a wide range of readers, including scientific and technical personnel, enterprise supervisors, and postgraduates and college students of colleges and universities engaged in the fields of national defence, military, aviation, aerospace, communications, electronics, automotive, agriculture, meteorology, environmental protection, remote sensing, mapping, oil field, construction, transportation, finance, telecommunications, education, medical care, film and television, and art. Journal of Image and Graphics is included in many important domestic and international scientific literature database systems, including EBSCO database in the United States, JST database in Japan, Scopus database in the Netherlands, China Science and Technology Thesis Statistics and Analysis (Annual Research Report), China Science Citation Database (CSCD), China Academic Journal Network Publishing Database (CAJD), and China Academic Journal Network Publishing Database (CAJD). China Science Citation Database (CSCD), China Academic Journals Network Publishing Database (CAJD), China Academic Journal Abstracts, Chinese Science Abstracts (Series A), China Electronic Science Abstracts, Chinese Core Journals Abstracts, Chinese Academic Journals on CD-ROM, and China Academic Journals Comprehensive Evaluation Database.
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