A Screen-Shooting Resilient Document Image Watermarking Scheme using Deep Neural Network
This addresses the security issue of document leakage via screen-capturing for organizations handling sensitive information, representing a novel method for a known bottleneck in digital watermarking.
The paper tackles the problem of protecting confidential documents displayed on screens from unauthorized camera capture by proposing a deep neural network-based watermarking scheme that embeds and extracts watermarks resilient to screen-shooting distortions. Experimental results show it achieves higher robustness and visual quality than three recent state-of-the-art methods, maintaining high extraction accuracy even under extreme shooting distances and angles.
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.