基于双域标记的视频鲁棒可逆水印算法

doi:10.3969/j.issn.1671-1122.2024.02.007

摘要/Abstract

摘要：

针对现有视频水印算法无法兼顾鲁棒性与可逆性的问题，文章提出一种基于双域标记的视频鲁棒可逆水印算法。该算法在H.264视频编码中的量化DCT系数域利用传统鲁棒水印拼接技术，嵌入水印信息;在运动矢量域利用二维直方图迁移技术，嵌入辅助信息，实现解码端的水印提取与原始视频的无损恢复。实验结果表明，文章算法具有良好的不可见性，实验视频的峰值信噪比与结构相似度均值分别为44.7537 dB与0.9902，比特率扩张均在16.74%以下，同时对不同强度的失真攻击均具有强鲁棒性，实验视频的归一化互相关系数均在0.970以上，误码率均在0.068以下。

关键词: 鲁棒可逆水印, 双域标记, H.264编码标准, 量化DCT系数, 运动矢量

Abstract:

Aiming at the compatibility problem of robustness and reversibility of existing video watermarking algorithms, a video robust reversible watermarking algorithm based on dual-domain marking was proposed. This algorithm used traditional robust watermark splicing technology to embed watermark information in the quantized DCT coefficient domain of H.264 video coding; it used two-dimensional histogram migration technology in the motion vector domain to embed auxiliary information to achieve watermark extraction at the decoder and lossless recovery of the original video. Experimental results show that the algorithm in this paper has good invisibility. The peak signal-to-noise ratio and average structural similarity of the experimental video are 44.7537 dB and 0.9902 respectively. The bit rate expansion is all at 16.74% and below. At the same time, it is resistant to distortion attacks of different strengths. It has strong robustness. The normalized cross-correlation coefficients of experimental videos are all above 0.970, and the bit error rates are below 0.068.

Key words: robust reversible watermarking, dual domain embed, H.264 encoding standard, quantized DCT coefficient, motion vector

中图分类号:

TP309

钮可, 梁钰承, 孟逸飞, 汪晶晶. 基于双域标记的视频鲁棒可逆水印算法[J]. 信息网络安全, 2024, 24(2): 239-251.

NIU Ke, LIANG Yucheng, MENG Yifei, WANG Jingjing. Dual Embedding Domain Based Video Robust Reversible Watermarking Algorithm[J]. Netinfo Security, 2024, 24(2): 239-251.

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序列视频	Carphone	CITY	Foreman	Mobile	News
PSNR/dB	$\infty $	$\infty $	$\infty $	$\infty $	$\infty $
SSIM	1	1	1	1	1

序列视频	Tree	Street	Food
PSNR/dB	$\infty $	$\infty $	$\infty $
SSIM	1	1	1

序列视频	Carphone	CITY	Foreman	Mobile	News
帧数	150	150	150	150	150
MPSNR/dB	45.1408	41.9119	45.8778	41.8662	43.5178
MSSIM	0.9996	0.9982	0.9995	0.9971	0.9986

序列视频	Tree	Street	Food
帧数	150	150	150
MPSNR/dB	49.7482	44.9500	45.0166
MSSIM	0.9999	0.9996	0.9900

序列视频	本文算法		文献[21]算法		文献[16]算法
序列视频	视频比特率扩张	嵌入信息量与视频增量比值	视频比特率扩张	嵌入信息与视频增量比值	视频比特率扩张	嵌入信息与视频增量比值
Carphone	12.89%	1.11	13.44%	1.09	13.24%	1.09
CITY	10.83%	1.13	10.79%	1.12	11.12%	1.10
Foreman	16.74%	1.02	17.99%	1.01	17.53%	1.02
Mobile	7.14%	1.16	8.23%	1.15	7.67%	1.15
News	12.77%	1.10	13.12%	1.09	14.21%	1.08
Tree	8.01%	1.15	9.13%	1.13	8.47%	1.14
Street	11.95%	1.12	12.61%	1.10	13.09%	1.09
Food	10.83%	1.14	12.01%	1.11	11.38%	1.12