Open Access

ARTICLE

Image Watermarking Algorithm Base on the Second Order Derivative and Discrete Wavelet Transform

Maazen Alsabaan¹, Zaid Bin Faheem², Yuanyuan Zhu², Jehad Ali^3,*

1 Department of Computer Engineering, College of Computer and Information Sciences (CCIS), King Saud University, Riyadh, 11451, Saudi Arabia
2 Department of Computer Science, Wuhan University, Wuhan, 430000, China
3 Department of AI Convergence Network, Ajou University, Suwon, 16499, Republic of Korea

* Corresponding Author: Jehad Ali. Email:

Computers, Materials & Continua 2025, 84(1), 491-512. https://doi.org/10.32604/cmc.2025.064971

Received 28 February 2025; Accepted 29 April 2025; Issue published 09 June 2025

Abstract

Image watermarking is a powerful tool for media protection and can provide promising results when combined with other defense mechanisms. Image watermarking can be used to protect the copyright of digital media by embedding a unique identifier that identifies the owner of the content. Image watermarking can also be used to verify the authenticity of digital media, such as images or videos, by ascertaining the watermark information. In this paper, a mathematical chaos-based image watermarking technique is proposed using discrete wavelet transform (DWT), chaotic map, and Laplacian operator. The DWT can be used to decompose the image into its frequency components, chaos is used to provide extra security defense by encrypting the watermark signal, and the Laplacian operator with optimization is applied to the mid-frequency bands to find the sharp areas in the image. These mid-frequency bands are used to embed the watermarks by modifying the coefficients in these bands. The mid-sub-band maintains the invisible property of the watermark, and chaos combined with the second-order derivative Laplacian is vulnerable to attacks. Comprehensive experiments demonstrate that this approach is effective for common signal processing attacks, i.e., compression, noise addition, and filtering. Moreover, this approach also maintains image quality through peak signal-to-noise ratio (PSNR) and structural similarity index metrics (SSIM). The highest achieved PSNR and SSIM values are 55.4 dB and 1. In the same way, normalized correlation (NC) values are almost 10%–20% higher than comparative research. These results support assistance in copyright protection in multimedia content.

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Keywords

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