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Dataset for file fragment classification of video file formats

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 vi...

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Autores principales: Sadeghi, Narges, Fahiminia, Mohadeseh, Teimouri, Mehdi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7160908/
https://www.ncbi.nlm.nih.gov/pubmed/32293534
http://dx.doi.org/10.1186/s13104-020-05037-x
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author Sadeghi, Narges
Fahiminia, Mohadeseh
Teimouri, Mehdi
author_facet Sadeghi, Narges
Fahiminia, Mohadeseh
Teimouri, Mehdi
author_sort Sadeghi, Narges
collection PubMed
description 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.
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spelling pubmed-71609082020-04-21 Dataset for file fragment classification of video file formats Sadeghi, Narges Fahiminia, Mohadeseh Teimouri, Mehdi BMC Res Notes Data Note 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. BioMed Central 2020-04-15 /pmc/articles/PMC7160908/ /pubmed/32293534 http://dx.doi.org/10.1186/s13104-020-05037-x Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Data Note
Sadeghi, Narges
Fahiminia, Mohadeseh
Teimouri, Mehdi
Dataset for file fragment classification of video file formats
title Dataset for file fragment classification of video file formats
title_full Dataset for file fragment classification of video file formats
title_fullStr Dataset for file fragment classification of video file formats
title_full_unstemmed Dataset for file fragment classification of video file formats
title_short Dataset for file fragment classification of video file formats
title_sort dataset for file fragment classification of video file formats
topic Data Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7160908/
https://www.ncbi.nlm.nih.gov/pubmed/32293534
http://dx.doi.org/10.1186/s13104-020-05037-x
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