Global adaptive watermarking algorithm based on Bayesian optimization

This paper presents a novel global adaptive watermarking algorithm that utilizes Bayes optimization to address the limitations of traditional fixed parameter watermarking algorithms in terms of robustness, invisibility, and embedding efficiency. The proposed algorithm dynamically adjusts watermarking parameters using Bayesian optimization, resulting in improved performance in various scenarios. The algorithm's performance was evaluated through a series of experiments, comparing it with the traditional fixed parameter watermarking algorithm. The experimental results demonstrate that the adaptive watermarking algorithm based on Bayesian optimization significantly enhances watermark invisibility, robustness, and extraction accuracy. By precisely adjusting the parameters, the algorithm achieves superior performance across different media content and attack scenarios.