Particle Swarm Bacterial Foraging Optimization method for Enhanced digital image watermarking system for data security comparison with Genetic algorithm
Deepthi Pula, R. Puviarasi · 2023
The primary objective of this study is to enhance the data security of digital image watermarking systems through the application of Bacterial Foraging with Particle Swarm Optimization (BF-PSO) and compare Peak Signal Noise Ratio (PSNR) with a Genetic algorithm (GA). The dataset in this paper utilizes the publicly available Kaggle database. The sample size for analysing the data security in a digital image watermarking system with enhanced PSNR was 20 (Group 1 = 10 and Group 2 = 10), and calculations were conducted using G-power 0.8, alpha and beta values of 0.05 and 0.2, and a 95% confidence interval. Bacterial foraging with particle swarm optimization (BF-PSO) and while number of samples (N=10) and Genetic algorithm (GA), where number of samples (N=10) are taken into consideration are used to analyze the digital image watermarking system with improved PSNR. The novel Bacterial Foraging with Particle Swarm Optimization (BF-PSO) has 58.30 higher PSNR when compared to the PSNR of Genetic algorithm (GA) is 40.55. The significance level of the study is p<0.05, or p=0.037. Bacterial Foraging with Particle Swarm Optimization (BF-PSO) in comparison to Genetic algorithm (GA) yields superior results in Peak Signal Noise Ratio (PSNR) when it comes to improving digital image watermarking systems and ensuring the safety of the data.