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Dataset for file fragment classification of image file formats
Reyhane Fakouri1, Mehdi Teimouri2
1Information Theory and Coding Laboratory, University of Tehran, Tehran, Iran.
BMC Research Notes
|November 29, 2019
Summary
Researchers created a new dataset for classifying image file format fragments, crucial for network forensics. This dataset enables performance comparisons of file fragment classification methods.
Area of Science:
- Computer Science
- Digital Forensics
Background:
- File fragment classification is vital for network forensics.
- Existing public datasets lack image file format fragments, hindering method comparison.
- A standardized dataset is needed to evaluate file fragment classification techniques.
Purpose of the Study:
- To introduce a novel dataset for image file format fragments.
- To facilitate comparative analysis of file fragment classification algorithms.
- To address the scarcity of public data in this specialized forensic area.
Main Methods:
- The study presents a dataset comprising 25,600 file fragments.
- Fragments cover ten image file formats: Bitmap, Better Portable Graphics, Free Lossless Image Format, Graphics Interchange Format, Joint Photographic Experts Group (JPEG), JPEG 2000, JPEG Extended Range, Portable Network Graphic, Tagged Image File Format, and Web Picture.
- Each format includes fragments from various compression settings, with 800 fragments per format-compression pair.
Main Results:
- A comprehensive dataset of image file format fragments has been successfully compiled.
- The dataset provides a standardized resource for evaluating forensic analysis tools.
- This resource supports research in network forensics and file system analysis.
Conclusions:
- The developed dataset fills a critical gap in publicly available forensic resources.
- It enables reproducible research and benchmarking of file fragment classification methods.
- This contributes to advancing the field of network forensics through standardized data.
