| 引用本文: | 公衍超,王子琳,杨楷芳,刘颖,林庆帆,王富平.色度域亮度域信息融合的监控视频重压缩取证[J].哈尔滨工业大学学报,2024,56(5):46.DOI:10.11918/202212091 |
| GONG Yanchao,WANG Zilin,YANG Kaifang,LIU Ying,LIM Kengpang,WANG Fuping.Video forensic compression with chroma and luminance domain information fusion for surveillance videos[J].Journal of Harbin Institute of Technology,2024,56(5):46.DOI:10.11918/202212091 |
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| 色度域亮度域信息融合的监控视频重压缩取证 |
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公衍超1,2,3,王子琳1,杨楷芳4,刘颖1,2,3,林庆帆1,5,王富平1,2,3
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(1.西安邮电大学 通信与信息工程学院,西安 710121; 2.西安邮电大学 陕西省法庭科学电子信息实验研究中心,西安 710121; 3.西安邮电大学 陕西省无线通信与信息处理技术国际联合研究中心,西安 710121; 4.陕西师范大学 计算机科学学院,西安 710119; 5.新加坡XsecPro公司,新加坡 787820)
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| 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%。 |
| 关键词: 视频编码 通用视频编码 重压缩取证 监控视频 色度域 |
| DOI：10.11918/202212091 |
| 分类号:TN911.7 |
| 文献标识码:A |
| 基金项目:国家自然科学基金(62277036) |
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| Video forensic compression with chroma and luminance domain information fusion for surveillance videos |
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GONG Yanchao1,2,3,WANG Zilin1,YANG Kaifang4,LIU Ying1,2,3,LIM Kengpang1,5,WANG Fuping1,2,3
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(1.School of Communications and Information Engineering, Xian University of Posts and Telecommunications, Xian 710121, China; 2.Electronic Information Experimental Research Center for Forensic Science of Shaanxi Province, Xian University of Posts and Telecommunications, Xian 710121, China; 3.International Joint Research Center for Wireless Communication and Information Processing Technology of Shaanxi Province, Xian University of Posts and Telecommunications, Xian 710121, China; 4.School of Computer Science, Shaanxi Normal University, Xian 710119, China; 5.XsecPro Pte. Ltd., Singapore 787820, Singapore)
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| 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. |
| Key words: video coding versatile video coding recompression forensics surveillance video chroma domain |
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