.png){kind=icon}
.png){kind=icon}
Return
A time-efficient optimization for robust image watermarking using machine learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2018.02.002.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Watermarking is utilized for securing multimedia data exchange. The techniques used to embed a watermark typically require optimizing scheme parameters often through applying meta-heuristic optimization techniques. Although meta-heuristic techniques have been widely used due to their performance enhancing capability, they are not suitable for time sensitive applications due to their large time consumption. In this paper, a time-efficient optimization based on machine learning is proposed to find the best embedding strength parameter for robust image watermarking in terms of both the imperceptibility and robustness. First, a watermark embedding scheme is designed in the Discrete Cosine Transform domain. which provide a proper robustness against common watermarking attacks. Then, a training process is performed through selecting set of images upon which the watermarking is applied and optimized using the Artificial Bee Colony algorithm. Observation data is collected, which includes the optimum strength values along with the feature vectors that represent the training images. The features, of an image, are extracted by calculating the optimization fitness function at different values of the embedding strength. Finally, new set of images are chosen to be watermarked using the optimum embedding parameters that are predicted through the K-Nearest Neighborhood regression method. Experimental results show that the proposed method consumes considerably less time to evaluate the optimum solutions compared to using meta-heuristic optimization. Meanwhile, the error between the optimum and predicted optimum solutions has negligible impact on the optimization objectives. (C) 2018 Elsevier Ltd. All rights reserved.
Keywords:
Meta-heuristic optimization
Machine learning
Robust image watermarking
Discrete Cosine Transform
Artificial Bee Colony
K-Nearest Neighborhood
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W