## 3 Proposed Technique
### Watermark Extraction Process
The regulations in question serve to screen the archive, utilising specific criteria, including density [56], in order to maintain the archive's size below the maximum permissible limit. In cases where the archive has reached maximum capacity, the superior solution, as determined by the designated metric, supplants the archive member with the lowest assigned value. The guidance mechanism plays a crucial role in the identification of optimal solutions by determining the trajectory of particles within the search space. Particles are provided with direction through the utilisation of the global best (gbest) and personal best (pbest). The performance of gbest is contingent upon the imperceptibility of the watermarked images, whereas pbest is contingent upon the structural similarity index (SSIM) and normalized correlation (NC) of the extracted watermark following a series of image processing attacks. In order to reduce the trade-off between robustness and imperceptibility, a fitness function based on PSNR, SSIM, and NC was derived in Equation (12). \[fitness=|PSNR-\Delta|+\left[\left(1-\frac{1}{N}\sum_{i=1}^{N}\alpha_{i}\right)+ \left(1-\frac{1}{N}\sum_{i=1}^{N}\beta_{i}\right)\right] \tag{12}\]

Figure 4: Block Diagram: Watermark Extraction Process. where \(N\) is the number of attacks, \(\alpha\) and \(\beta\) is \(NC\) and \(SSIM\) values of extracted watermark, respectively, and \(\Delta\) is the optimization parameter to guarantee the optimum imperceptibility of the watermarked image. ## 4 Simulation Results and Discussion

This section examines the anticipated work and discusses the results. Various types of colour ultrasound images and binary watermark images have been used to test how well the method works. The proposed work was simulated using MATLAB R2015a on a Windows 11 machine with 16 GB of RAM and a core i7 processor. This allowed us to assess the effectiveness of the proposed technique in terms of imperceptibility and robustness. The selected cover medical image has dimensions of \(1024\times 768\) pixels, sourced from The National Library of Medicine image database ([[https://medpix.nlm.nih.gov](https://medpix.nlm.nih.gov)]([https://medpix.nlm.nih.gov](https://medpix.nlm.nih.gov)), accessed on 12 April 2023). Additionally, the binary watermark has dimensions of \(64\times 64\) pixels. Figure 5 depicts the test medical images as well as the watermark. Figure 6 shows the watermarked image and the retrieved watermark. It shows that the proposed watermark embedding and extraction technique works well. Each experimental image's caption includes the PSNR value of the watermarked image. The normalized cross co-relation values are also displayed as the captions for the retrieved watermark images. Table 1 displays the PSNR and SSIM values of the watermarked images, along with the NC and SSIM values of the extracted watermark. The maximum peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) values achieved are 55.9115 and 0.9898, respectively. The watermarked image effectively showcases the robust imperceptibility of the watermark. Moreover, the technique for extracting watermarks is efficacious. The proximity of the NC and SSIM values to unity is indicative of the prof

Figure 5: Test set: Colour medical image and binary watermark. ### Robustness Results Evaluation

The efficacy of the watermarking methodology has been evaluated across various image processing benchmarks. The watermarked image undergoes the interpolation of image processing attacks, including but not limited to filtering, compression, and noise. Subsequently, the watermark is restored. The evaluation of a watermarking system's resilience is conducted through the computation of the NC and SSIM metrics for the recovered watermark. This section presents a review of the results obtained from various image-processing attacks.