View in EDS

Detection of transcoding from HEVC to VVC based on CU types and Motion Vector Map.

Saved in:
Bibliographic Details
Title: Detection of transcoding from HEVC to VVC based on CU types and Motion Vector Map.
Authors: Lin, Jiajian1 (AUTHOR), Zhang, Jianlin1 (AUTHOR), Bian, Shan1 (AUTHOR) bianshan@scau.edu.cn, Wang, Chuntao1 (AUTHOR)
Source: Applied Intelligence. Feb2026, Vol. 56 Issue 4, p1-19. 19p.
Subjects: Transcoding, Video coding, Support vector machines, Video compression standards, Digital forensics, MPEG (Video coding standard)
Abstract: The application of new coding standards brings a wider range of applications as well as new video forensic challenges. In order to solve the transcoding detection problem under the new video standard H.266/Versatile Video Coding (H.266/VVC), we propose a new algorithm based on Coding Unit (CU) block type classification and Motion Vector Map (MVM) statistics to detect VVC transcoded videos. We first analyze the generation of HEVC/VVC transcoded video traces and define MVM graphs for constructing features. By calculating the average frequency of different CU partition types for intra-frame prediction frames and inter-frame prediction frames, they are connected and sent as distinguishing features to a support vector machine for classification. Experimental results show that the method has high recognition accuracy under the new coding standard. Specifically, the algorithm constructs a 51-dimensional feature vector by combining intra-frame and inter-frame features, capturing transcoding traces such as changes in CU partitioning and motion vector distributions. Extensive experiments on 1080p and 4K video datasets, across various bitrate combinations, quantization parameters, and Group of Pictures (GOP) structures, demonstrate an average detection accuracy of 96.81% on 1080p datasets, outperforming existing methods. The robustness of the proposed approach across diverse encoding configurations highlights its effectiveness for forensic analysis under the VVC standard. [ABSTRACT FROM AUTHOR]
Copyright of Applied Intelligence is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Description
Abstract:The application of new coding standards brings a wider range of applications as well as new video forensic challenges. In order to solve the transcoding detection problem under the new video standard H.266/Versatile Video Coding (H.266/VVC), we propose a new algorithm based on Coding Unit (CU) block type classification and Motion Vector Map (MVM) statistics to detect VVC transcoded videos. We first analyze the generation of HEVC/VVC transcoded video traces and define MVM graphs for constructing features. By calculating the average frequency of different CU partition types for intra-frame prediction frames and inter-frame prediction frames, they are connected and sent as distinguishing features to a support vector machine for classification. Experimental results show that the method has high recognition accuracy under the new coding standard. Specifically, the algorithm constructs a 51-dimensional feature vector by combining intra-frame and inter-frame features, capturing transcoding traces such as changes in CU partitioning and motion vector distributions. Extensive experiments on 1080p and 4K video datasets, across various bitrate combinations, quantization parameters, and Group of Pictures (GOP) structures, demonstrate an average detection accuracy of 96.81% on 1080p datasets, outperforming existing methods. The robustness of the proposed approach across diverse encoding configurations highlights its effectiveness for forensic analysis under the VVC standard. [ABSTRACT FROM AUTHOR]
ISSN:0924669X
DOI:10.1007/s10489-026-07134-z