With the increasing maturity of video editing technology, forgers are more inclined to transcode videos to High Efficiency Video Coding (HEVC) videos, as HEVC not only enables people to enjoy high-definition videos but also allows broadcasters to stream it more efficiently across networks. Therefore, to verify the originality and authenticity, it is of great significance to propose an algorithm for detecting transcoded HEVC videos. In this paper, a theoretical model of video transcoding is first constructed, and a novel transcoding detection algorithm based on In-loop Filtering and Prediction Units (PU) Partition (IFPP) is proposed. By analyzing the statistical characteristics of strong and normal filtering modes in deblocking filtering and calculating offset values in Sample Adaptive Offset (SAO) filtering, the transcoding traces in inter-coded frames can be captured. In addition, PU partition statistics are also extracted to make full use of traces in intra-coded frames. By fusing these subfeatures, the proposed IFPP feature with 17 dimensions can be obtained, which is further fed to the Support Vector Machine (SVM) classifier. Finally, experiments are conducted on datasets with various coding parameters. Results show that the proposed algorithm outperforms existing algorithms and has better robustness.

随着视频编辑技术的日趋成熟，伪造者更倾向于将视频转码为高效视频编码（HEVC）视频，因为HEVC不仅使人们能够欣赏高清视频，而且使广播公司可以更有效地跨网络流式传输视频。因此，验证原始性和真实性，提出一种检测转码HEVC视频的算法具有重要意义。本文首先建立了视频转码的理论模型，并提出了一种基于环内滤波和预测单元（IFPP）划分的新的转码检测算法。通过分析解块滤波中强滤波模式和普通滤波模式的统计特性，并计算样本自适应偏移（SAO）滤波中的偏移值，可以捕获帧间编码帧中的代码转换轨迹。此外，还提取了PU分区统计信息，以充分利用帧内编码帧中的跟踪。通过融合这些子功能，可以获得具有17个维度的拟议IFPP功能，并将其进一步馈入支持向量机（SVM）分类器。最后，对具有各种编码参数的数据集进行了实验。结果表明，该算法优于现有算法，具有较好的鲁棒性。对具有各种编码参数的数据集进行了实验。结果表明，该算法优于现有算法，具有较好的鲁棒性。对具有各种编码参数的数据集进行了实验。结果表明，该算法优于现有算法，具有较好的鲁棒性。