Abstract
DECA, a novel file carving application, is an example of digital forensic tools that rely heavily on accurately detecting the type of data fragments stored in the disk blocks. This work is an attempt to create a method of detection and classification of PNG data types for DECA which originally only identifies and extracts JPEG data. The PNG file format was examined in order to implement the PNG data detector that was integrated into DECA. We then examined the results of decision-theoretic file carving, implemented in DECA, combined with the implemented PNG data detector.
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