In this paper, we propose an algorithm for detecting frame deletion in HEVC-coded video in the compressed domain. Specifically, we focus on the frame type changes occurring upon frame deletion, which cause slight differences between the coding patterns in original and forged video. Then, we identify discriminating coding patterns for use as features, which are classified by machine learning classifiers. Furthermore, we propose video sequence integrity detection on a group-of-picture basis, which is computationally efficient and robust even when applied to static videos and videos with genuine scene changes. The experimental results show that the proposed technique can classify HEVC-coded videos more accurately than previous methods. In addition, the results demonstrate that the selected features work harmoniously in discrimination and that the learning-based classifiers are more robust and reliable than model-based classifiers.

在本文中，我们提出了一种在压缩域中检测HEVC编码视频中帧删除的算法。具体来说，我们专注于在删除帧时发生的帧类型变化，这些变化会导致原始视频和伪造视频中的编码模式略有不同。然后，我们将识别出的编码模式用作特征，并由机器学习分类器对其进行分类。此外，我们提出了基于图片组的视频序列完整性检测的方法，即使将其应用于静态视频和具有真实场景变化的视频，该算法在计算上也非常有效且可靠。实验结果表明，所提出的技术比以前的方法能够更准确地分类HEVC编码的视频。此外，