A Screen-Shooting Resilient Document Image Watermarking Scheme using Deep Neural Network

TL;DR

This paper introduces a deep neural network-based watermarking scheme for document images that remains robust against screen-shooting distortions, ensuring watermark extraction even under extreme shooting conditions.

Contribution

It presents an end-to-end neural network with distortion simulation and adaptive embedding, enhancing robustness and visual quality over existing methods.

Findings

Higher robustness against camera distortions

Maintains high extraction accuracy at extreme shooting angles

Improves visual quality of watermarked images

Abstract

With the advent of the screen-reading era, the confidential documents displayed on the screen can be easily captured by a camera without leaving any traces. Thus, this paper proposes a novel screen-shooting resilient watermarking scheme for document image using deep neural network. By applying this scheme, when the watermarked image is displayed on the screen and captured by a camera, the watermark can be still extracted from the captured photographs. Specifically, our scheme is an end-to-end neural network with an encoder to embed watermark and a decoder to extract watermark. During the training process, a distortion layer between encoder and decoder is added to simulate the distortions introduced by screen-shooting process in real scenes, such as camera distortion, shooting distortion, light source distortion. Besides, an embedding strength adjustment strategy is designed to improve the visual quality of the watermarked image with little loss of extraction accuracy. The experimental results show that the scheme has higher robustness and visual quality than other three recent state-of-the-arts. Specially, even if the shooting distances and angles are in extreme, our scheme can also obtain high extraction accuracy.