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http://dx.doi.org/10.13089/JKIISC.2020.30.2.189

Classification of Non-Signature Multimedia Data Fragment File Types With Byte Averaging Gray-Scale  

Yoon, Hyun-ho (Best of the Best, Korea Information Technology Research Institute)
Kim, Jae-heon (Best of the Best, Korea Information Technology Research Institute)
Cho, Hyun-soo (Best of the Best, Korea Information Technology Research Institute)
Won, Jong-eun (Best of the Best, Korea Information Technology Research Institute)
Kim, Gyeon-woo (Best of the Best, Korea Information Technology Research Institute)
Cho, Jae-hyeon (Best of the Best, Korea Information Technology Research Institute)
Publication Information
Journal of the Korea Institute of Information Security & Cryptology / v.30, no.2, 2020 , pp. 189-196 More about this Journal
Abstract
In general, fragmented files without signatures and file meta-information are difficult to recover. Multimedia files, in particular, are highly fragmented and have high entropy, making it almost impossible to recover with signature-based carving at present. To solve this problem, research on fragmented files is underway, but research on multimedia files is lacking. This paper is a study that classifies the types of fragmented multimedia files without signature and file meta-information. Extracts the characteristic values of each file type through the frequency differences of specific byte values according to the file type, and presents a method of designing the corresponding Gray-Scale table and classifying the file types of a total of four multimedia types, JPG, PNG, H.264 and WAV, using the CNN (Convolutional Natural Networks) model. It is expected that this paper will promote the study of classification of fragmented file types without signature and file meta-information, thereby increasing the possibility of recovery of various files.
Keywords
CNN; Digital Forensics; Data Fragment Classification;
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