An enhanced time efficient technique for image watermarking using ant colony optimization and light gradient boosting algorithm

For a secure multimedia data exchange, watermarking is used. Generally, meta-heuristic optimization is required to optimize scheme parameters in the watermarking embedding. Even though meta-heuristic methods have been widely used because of their enhancing capability, due to their large time consump-tion nature they are not applicable for time-sensitive applications. This paper proposes a time efficient optimization method based on machine learning algorithms to detect the best embedding parameter for image watermarking with both robustness and imperceptibility. Initially, for providing robustness against watermarking attacks, a method for watermarking embedding is designed in the domain of Discrete Cosine Transform. After that, Ant Colony optimization is used for finding the optimization parameters. Then an observation data is obtained, which includes the feature vector and optimum strength values of the training images. The image features are extracted at different embedded strength values by the calculation of optimum fitness function. Finally Light Gradient Boosting algorithm (LGBA) is applied to predict the optimum embedding parameters of the set of new images which are to be water -marked. When compared with the existing optimization methods, it has been found that the proposed method consumes very less time for the evaluation of optimum solutions. From the results, it has been identified that the proposed algorithm satisfies the image watermarking with the improvement in time enhancement. The effectiveness of the proposed method is analyzed using MATLAB 2018b.(c) 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

This paper proposes a time efficient optimization method based on machine learning algorithms for detecting the best embedding parameter for image watermarking. The method utilizes watermarking embedding in the domain of Discrete Cosine Transform, Ant Colony optimization for finding optimization parameters, observation data including feature vectors and optimum strength values, and Light Gradient Boosting algorithm for predicting optimum embedding parameters. The proposed method shows higher efficiency compared to existing methods.