# Docmarking: Real-Time Screen-Cam Robust Document Image Watermarking

arXiv: https://arxiv.org/abs/2304.12682 · HTML: https://mlsanity.com/paper/2304.12682/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 26 references that are in the map.
- First posted on arXiv 25 Apr 2023.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale](https://mlsanity.com/paper/1811.00982/) (2018)
- [Moir'e Photo Restoration Using Multiresolution Convolutional Neural Networks](https://mlsanity.com/paper/1805.02996/) (2018)
- [Omnidirectional CNN for Visual Place Recognition and Navigation](https://mlsanity.com/paper/1803.04228/) (2018)
- [NTIRE 2020 Challenge on Image Demoireing: Methods and Results](https://mlsanity.com/paper/2005.03155/) (2020)
- [A Screen-Shooting Resilient Document Image Watermarking Scheme using Deep Neural Network](https://mlsanity.com/paper/2203.05198/) (2022)

## Built on by (most cited first, 1 of 1)

- [Watermarks Attack Watermarks: Re-Watermarking as a Generic Removal Strategy](https://mlsanity.com/paper/2605.16796/) (2026)

## Abstract (arXiv)

This paper focuses on investigation of confidential documents leaks in the form of screen photographs. Proposed approach does not try to prevent leak in the first place but rather aims to determine source of the leak. Method works by applying on the screen a unique identifying watermark as semi-transparent image that is almost imperceptible for human eyes. Watermark image is static and stays on the screen all the time thus watermark present on every captured photograph of the screen. The key components of the approach are three neural networks. The first network generates an image with embedded message in a way that this image is almost invisible when displayed on the screen. The other two neural networks are used to retrieve embedded message with high accuracy. Developed method was comprehensively tested on different screen and cameras. Test results showed high efficiency of the proposed approach.