Advanced Dipper-Throated Meta-Heuristic Optimization Algorithm for Digital Image Watermarking

Abstract

Recently, piracy and copyright violations of digital content have become major concerns as computer science has advanced. In order to prevent unauthorized usage of content, digital watermarking is usually employed. This work proposes a new approach to digital image watermarking that makes use of the discrete cosine transform (DCT), discrete wavelet transform (DWT), dipper-throated optimization (DTO), and stochastic fractal search (SFS) algorithms. The proposed approach involves computing the discrete wavelet transform (DWT) on the cover image to extract its sub-components, followed by the performance of a discrete cosine transform (DCT) to convert these sub-components into the frequency domain. Finding the best scale factor for watermarking is a significant challenge in most watermarking methods. The authors used an advanced optimization algorithm, which is referred to as DTOSFS, to determine the best two parameters—namely, the scaling factor and embedding coefficient—to be used while inserting a watermark into a cover image. Using the optimal values of these parameters, a watermark image can be inserted into a cover image more efficiently. The suggested approach is evaluated in comparison with the current gold standard. The normalized cross-correlation (NCC), peak-signal-to-noise ratio (PSNR), and image fidelity (IF) are used to measure the success of the proposed approach. In addition, a statistical analysis is performed to evaluate the significance and superiority of the proposed approach. The experimental results confirm the effectiveness of the proposed approach in improving upon standard watermarking methods based on the DWT and DCT. Moreover, a set of attacks is considered to study the robustness of the proposed approach, and the results confirm the expected outcomes. It is shown by the achieved results that the proposed approach can be utilized for practical digital image watermarking, and that it significantly outperforms other digital image watermarking methods.

Improved slime mould algorithm based on hybrid strategy optimization of Cauchy mutation and simulated annealing

“…Referring to the application scenarios of other optimization algorithms, the slime mold algorithm can also do a lot. For example, an improved whale optimization algorithm for COVID-19 medical feature selection [ 46 ]; a dynamic arithmetic optimization algorithm for Tesla optimization under natural frequency constraints [ 47 ]; Dynamic Water Strider Algorithm for optimal design of bone structure [ 48 ]; A Quantum-Based Avian Navigation Optimizer for feature selection of high-dimensional medical data [ 49 ]; Multi-trial vector differential evolution algorithm for non-decomposition large-scale global optimization [ 50 ]; An Advanced Dipper-Throated Meta-Heuristic Optimization Algorithm for Digital Image Watermarking [ 51 ]; A Multi-Search Arithmetic Optimization Algorithm for Efficient Text Document Clustering [ 52 ]; An Effective Whale Optimization Algorithm to Solve the Optimal Power Flow Problem [ 53 ], etc.…”

Robust Image Watermarking in Spatial Domain Utilizing Features Equivalent to SVD Transform

“…With the help of side information V w from the watermark insertion phase and by the principal component obtained in Equation (7), the watermark can be extracted by the following equation:…”

“…However, this has raised concerns about the unauthorized copying of copy-protected media, and its protection is a challenging task [1][2][3]. The digital watermarking scheme was developed to handle such issues [4][5][6][7][8], and since then, it has been developed into a novel area of study and is generating substantial attention among scientists [9,10]. Watermarking is a relatively new field of study that provides effective solutions for a number of security applications, such as authentication and copyright protection [11,12].…”