Dataset for file fragment classification of video file formats

Abstract

Abstract Objectives File fragment classification of video file formats is a topic of interest in network forensics. There are some publicly available datasets for file fragments of various file types such as textual, audio, and image file formats. However, there is no public dataset for file fragments of video file formats. So, in order to evaluate and compare the performance of the classification methods, a challenge is the need to have such datasets. Data description In this study, we present a dataset that contains file fragments of 10 video file formats: 3GP, AVI, ASF, FLV, MKV, MOV, MP4, WebM, OGV, and RMVB. Corresponding to each format, the dataset contains the file fragments of video files with different video codec types: H.263, MPEG-4, WMV, H.264, FLV1, H.265, VP8, VP9, Theora, and RealVideo. Totally, 20 different pairs of video format and codec are employed. For each pair of video format and codec, 30,000 file fragments are provided. Totally, the dataset contains 600,000 file fragments.