ITC-MNP: a diverse dataset for image file fragment classification

The dataset includes image file fragments of 4096 bytes from five formats (JPG, BMP, GIF, PNG, and TIFF), each processed with different conversion settings. The source images are categorized into three content types: Nature, People, and Medical. In total, the dataset contains 501,000 fragments. These fragments consist of file headers and incomplete end-of-file fragments, completed with random bytes to approximate how operating systems handle data when file sizes are not multiples of the sector size. This approach aims to simulate typical scenarios where fragments are recovered from a hard drive, though it may not capture all real-world complexities such as data corruption and complex file structures.

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Publication Details

Journal
BMC Research Notes
Published
2024-12-19
DOI
https://doi.org/10.1186/s13104-024-07034-w
Citations
3
Primary Topic
Digital Media Forensic Detection
Type
article
Field-Weighted Citation Impact
0.75
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article

ITC-MNP: a diverse dataset for image file fragment classification

Babak Tavassoli, Mehdi Teimouri, Zhino Naghshbandi
3 citations
BMC Research Notes
Digital Media Forensic Detection
0.75
article

ITC-MNP: a diverse dataset for image file fragment classification

Babak Tavassoli, Mehdi Teimouri, Zhino Naghshbandi
article en
3 citations

Abstract

The dataset includes image file fragments of 4096 bytes from five formats (JPG, BMP, GIF, PNG, and TIFF), each processed with different conversion settings. The source images are categorized into three content types: Nature, People, and Medical. In total, the dataset contains 501,000 fragments. These fragments consist of file headers and incomplete end-of-file fragments, completed with random bytes to approximate how operating systems handle data when file sizes are not multiples of the sector size. This approach aims to simulate typical scenarios where fragments are recovered from a hard drive, though it may not capture all real-world complexities such as data corruption and complex file structures.

BMC Research NotesVol. 17(1)
University of Tehran (IR)
Peace, Justice and strong institutions
Openalex Percentile: Top 26%
Digital Media Forensic Detection
0.75
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