Adaptive Control of Embedding Strength in Image Watermarking using Neural Networks
This work addresses copyright protection for digital media by providing an incremental improvement in watermarking techniques.
The paper tackled the problem of balancing robustness and transparency in digital image watermarking by proposing a framework that adaptively determines embedding strength for each sub-block using Mask R-CNN on the COCO dataset, resulting in a method that is robust against attacks and maintains good transparency.
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.