Abstract Video (e.g. CCTV) plays a crucial role in digital forensics. Existing video carving methods either assume the existence of file system information or resort to the impractical exhaustive matching for all pairs of fragments. In this paper, a coarse-to-fine two-stage semantic video carving approach is proposed to improve the efficiency and precision of content-based video carving. The proposed approach introduces a perceptual grouping stage to quickly group video fragments first based on the structural similarity of the fragments, followed by a precise stitching stage which sorts the fragments within each group depending on the pixel-level content of the fragments to reconstruct each original video file. The proposed approach can reduce the computational complexity and achieve a high carving precision since the two complementary methods used in two stages focus on different-scale features of video content. Experimental results based on a YouTube-8M video clip dataset show that the overall carving precision of the proposed approach is very high (e.g. 97.2% even when the number of mixed video fragments is increased to 288, which are from 36 video files with a fragmentation degree of 8). The overall carving time is 326.22 seconds, about 10 times lower than that of our previous optical flow-based approach.

中文翻译：

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数字取证中的粗到细两阶段语义视频雕刻方法

摘要视频（例如闭路电视）在数字取证中起着至关重要的作用。现有的视频雕刻方法要么假设文件系统信息的存在，要么对所有片段对进行不切实际的穷举匹配。在本文中，提出了一种由粗到细的两阶段语义视频雕刻方法，以提高基于内容的视频雕刻的效率和精度。所提出的方法引入了感知分组阶段，首先根据片段的结构相似性对视频片段进行快速分组，然后是精确拼接阶段，根据片段的像素级内容对每个组内的片段进行排序以重建每个原始片段视频文件。由于两个阶段使用的两种互补方法专注于视频内容的不同尺度特征，因此所提出的方法可以降低计算复杂度并实现高雕刻精度。基于 YouTube-8M 视频片段数据集的实验结果表明，所提出的方法的整体雕刻精度非常高（例如，即使将混合视频片段的数量增加到 288，也能达到 97.2%，这是来自 36 个带有碎片的视频文件）度 8）。整体雕刻时间为 326.22 秒，比我们之前基于光流的方法低约 10 倍。2%，即使混合视频片段的数量增加到 288，这是来自 36 个片段度为 8 的视频文件）。整体雕刻时间为 326.22 秒，比我们之前基于光流的方法低约 10 倍。2%，即使混合视频片段的数量增加到 288，这是来自 36 个片段度为 8 的视频文件）。整体雕刻时间为 326.22 秒，比我们之前基于光流的方法低约 10 倍。