Adaptive Control of Embedding Strength in Image Watermarking using Neural Networks

Mahnoosh Bagheri; Majid Mohrekesh; Nader Karimi; Shadrokh Samavi

Adaptive Control of Embedding Strength in Image Watermarking using Neural Networks

Multimedia 2020-01-13 v1 Computer Vision and Pattern Recognition Image and Video Processing

Authors: Mahnoosh Bagheri , Majid Mohrekesh , Nader Karimi , Shadrokh Samavi

Abstract

Digital image watermarking has been widely used in different applications such as copyright protection of digital media, such as audio, image, and video files. Two opposing criteria of robustness and transparency are the goals of watermarking methods. In this paper, we propose a framework for determining the appropriate embedding strength factor. The framework can use most DWT and DCT based blind watermarking approaches. We use Mask R-CNN on the COCO dataset to find a good strength factor for each sub-block. Experiments show that this method is robust against different attacks and has good transparency.

Keywords

image watermarking embedding strength blind watermarking dwt dct mask r-cnn robustness transparency

Cite

@article{arxiv.2001.03251,
  title  = {Adaptive Control of Embedding Strength in Image Watermarking using Neural Networks},
  author = {Mahnoosh Bagheri and Majid Mohrekesh and Nader Karimi and Shadrokh Samavi},
  journal= {arXiv preprint arXiv:2001.03251},
  year   = {2020}
}

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4 pages 5 figures

Adaptive Control of Embedding Strength in Image Watermarking using Neural Networks

Abstract

Keywords

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