A new approach for jpeg fragmentation point detection using sequential difference by segment (SDbS)
Digital Forensic is a computer science application and an investigation means, aimed at the prevention of illegal crimes by tracking and retrieving evidence left by cyber criminals. Image recovery is one of the techniques to obtain digital evidences. This technique works by recovering damaged or...
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| 格式: | Thesis |
| 语言: | English English English |
| 出版: |
2019
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| 在线阅读: | http://eprints.uthm.edu.my/676/1/24p%20TENGKU%20NORSUHAILA%20T%20AZMI.pdf http://eprints.uthm.edu.my/676/2/TENGKU%20NORSUHAILA%20T%20AZMI%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/676/3/TENGKU%20NORSUHAILA%20T%20AZMI%20WATERMARK.pdf |
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| 总结: | Digital Forensic is a computer science application and an investigation means, aimed
at the prevention of illegal crimes by tracking and retrieving evidence left by cyber
criminals. Image recovery is one of the techniques to obtain digital evidences. This
technique works by recovering damaged or corrupted digital evidences either
through traditional data recovery or file craving. The file craving technique for the
most part is to overcome the inherent weaknesses in the traditional data recovery
even without any information about the file system. There are many files that can be
recovered; images are the most commonly restored files. However, in some cases,
JPEG images may be fragmented when stored in the hard disk and it makes it
difficult to repair them due to the complexity of determining the fragmentation
points. In this research, a technique called Sequential Difference by Segment (SDbS)
has been proposed to overcome weaknesses regarding fragmentation detection point
of fragmented JPEG images. This technique methodology consists of three steps
namely the acquisition of datasets, image segmentation and fragmentation detection.
The findings from the third step are the main contributions in this research through
the discovery of approaches to detect the fragmentation points using deletion
quaternary search and Euclidean Distance to compare pixel values. SDbS has been
tested using standard datasets, DFRWS 2006 and DFRWS 2007. They have also
been validated using three parameters; average time taken, total image correctly
recovered and accuracy of detecting fragmentation point. Based on the results
obtained, SDbS has a 19.65% more accurate than SoD, and 0.35% faster than binary
search. In other words, SDbS is such a good alternative in addressing problems in
detecting the fragmentation points for fragmented JPEG images, compared to SoD
and binary search. |
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