# Adaptive Control of Embedding Strength in Image Watermarking using Neural Networks

Source: [Adaptive Control of Embedding Strength in Image Watermarking using Neural Networks](<https://www.sota2.com/research/paper/68fa66e41e209131cea12e2e>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:00:21.133Z

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

Published: 2020-01-09T23:08:34.000Z

## 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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2001.03251>)

[PDF](<https://arxiv.org/pdf/2001.03251.pdf>)