High-Quality Video Watermarking Based on Deep Neural Networks for Video with HEVC Compression

被引：7

作者：

Kaczynski, Maciej ^{[1
]}

Piotrowski, Zbigniew ^{[1
]}

Pietrow, Dymitr ^{[1
]}

机构：

[1] Mil Univ Technol, Fac Elect, PL-00908 Warsaw, Poland

来源：

SENSORS | 2022年 / 22卷 / 19期

关键词：

neural network; watermark; deep learning; property verification; copyright protection; video; HEVC; H; 265; YUV420; YUV420p;

D O I：

10.3390/s22197552

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

This article presents a method for transparent watermarking of high-capacity watermarked video under H.265/HEVC (High-Efficiency Video Coding) compression conditions while maintaining high-quality encoded image. The aim of this paper is to present a method for watermark embedding using neural networks under conditions of subjecting video to lossy compression of the HEVC codec using the YUV420p color model chrominance channel for watermarking. This paper presents a method for training a deep neural network to embed a watermark when a compression channel is present. The discussed method is characterized by high accuracy of the video with an embedded watermark compared to the original. The PSNR (peak signal-to-noise ratio) values obtained are over 44 dB. The watermark capacity is 96 bits for an image with a resolution of 128 x 128. The method enables the complete recovery of a watermark from a single video frame compressed by the HEVC codec within the range of compression values defined by the CRF (constant rate factor) up to 22.

引用

页数：21

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