色度域亮度域信息融合的监控视频重压缩取证

Video forensic compression with chroma and luminance domain information fusion for surveillance videos

摘要:

为解决当前视频重压缩取证方法没有考虑色度域信息、取证准确度低的问题,提出一种面向最新多用途视频编码（versatile video coding,VVC）标准色度域亮度域信息融合的监控视频重压缩取证方法（CLF-SVRF）。基于VVC标准的编码原理,从监控视频的色度域和亮度域维度分析并确定VVC视频码流中与压缩次数密切相关的基础码流特征;基础码流特征包括色度域和亮度域编码单元（coding unit,CU）的划分类型及预测模式;结合拉格朗日率失真优化技术分析随着压缩次数的增加,色度域亮度域CU划分类型和预测模式的变化;进一步确定色度域亮度域CU划分类型和预测模式可以作为检测视频压缩次数的基础码流特征;接着考虑视频监控应用对重压缩取证方法低复杂度的需求,基于色度域亮度域CU划分类型和预测模式构建低复杂度高级码流特征;将高级码流特征输入支持向量机完成监控视频的重压缩取证。实验结果表明,与当前先进方法相比,CLF-SVRF方法的监控视频重压缩取证准确度平均提升了13.53%,同时可以大幅度地降低重压缩取证耗时,重压缩取证时间平均减少了47.42%。

Abstract:

To address the issues of not considering chromatic domain information and low accuracy in current video froensic compression methods, a surveillance video recompression forensics method for the latest versatile video coding (VVC) standard based on the information fusion of chroma domain and luminance domain, referred to as CLF-SVRF, is proposed. Based on the coding principles of the VVC standard, the basic bitstream characteristics in VVC video bitstream that are closely related to compression time are analyzed and determined from the dimensions of chroma domain and luminance domain in surveillance videos. The basic bitstream characteristics include the partitioning type and prediction mode of coding unit (CU) in chroma domain and luminance domain. Combining with the Lagrangian rate distortion optimization technique, the variations in the partitioning type and prediction mode of CU in chroma domain and luminance domain as the compression time increases are analyzed. It is further determined that the partitioning type and prediction mode of CU in chroma domain and luminance domain can be used as basic bitstream characteristics for detecting the video compression time. Then, considering the requirement of video surveillance applications for low complexity forensics methods, a low complexity advanced bitstream characteristics is constructed based on the partitioning type and prediction mode of CU in chroma domain and luminance domain. The advanced bitstream characteristics are input into the support vector machine to complete the recompression forensics of surveillance videos. The experimental results show that compared with the current advanced methods, the CLF-SVRF method can improve the accuracy of surveillance video recompression forensics by 13.53% on average. At the same time, it can significantly reduce the time required for forensic recompression, and reduce the recompression forensics time by 47.42% on average.