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混合波形变换,K-平均单数值分解和空间记忆引导的猫群优化技术用于嵌入水标

Chirag Sharma¹, Arwinder Dhillon¹, Deepak Prashar^1,2

¹Department of Computer Science and Engineering (CSE), Lovely Professional University, Phagwara, 144411, India.

Scientific reports

|December 21, 2025

概括

本研究介绍了一种使用离散波纹转换 (DWT) 和K-Means单数值分解 (K-SVD) 的增强视频水印技术,该技术使用空间记忆引导猫群优化 (SMGCSO) 进行了优化. 该方法通过增加PSNR和减少BER来显著改善版权保护,以应对视频攻击.

科学领域:

数字图像和视频处理数字图像和视频处理
多媒体安全多媒体安全
人工智能在优化中的作用

背景情况:

越来越多地整合和使用多媒体数据需要强大的知识产权和版权保护机制.
现有的水印技术在维护质量和对各种视频处理攻击的稳定性方面面临挑战.

研究的目的:

开发一种改进的水印技术,用于嵌入压缩视频中的水印.
为了提高精确的水印,以最小的质量损失和对攻击的优越稳定性.

主要方法:

开发了一种新的水印技术,结合了离散波纹变换 (DWT) 和K-Means单数值分解 (K-SVD).
集成的空间记忆引导猫群优化 (SMGCSO) 优化了水印过程,避免了局部优化.
使用峰值信号对噪声比率 (PSNR) 和比特错误率 (BER) 度量来评估性能.

主要成果:

与基线DWT-K-SVD方法相比,DWT-K-SVD-SMGCSO技术显示出了显著的改进.
实现了大约28%的PSNR增加,表明更好的图像质量保存.
观察到BER下降了45%,这意味着水印的稳定性和准确性得到了提高.

结论:

拟议的DWT-K-SVD-SMGCSO水印技术提供了对视频处理攻击的优越抵抗力.
该技术提供了增强的水印稳定性,对于有效的视频版权保护至关重要.
这一进步有助于在数字时代确保多媒体内容的安全.

关键词:

版权保护版权保护离散波量变换是离散波量变换.峰值信号与噪声比率的比率.质量损失质量损失视频水印的使用方法

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