Difference between revisions of "File Carving:SmartCarving"

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'''SmartCarving''' is a [[File Carving|file carving]] technique to recover fragmented files first proposed by [[User:PashaPal|A. Pal]] and N. Memon in DFRWS 2008. SmartCarving utilizes a combination of structure based validation along with validation of each file's unique content. Results for the SmartCarving technique
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'''SmartCarving''' is a [[File Carving|file carving]] technique to recover fragmented files first proposed by [[User:PashaPal|A. Pal]], T. Sencar and [[User:NasirMemon|N. Memon]] in DFRWS 2008. The term '''smart carving''' was already proposed in 2006 in [http://sandbox.dfrws.org/2006/mora/dfrws2006.pdf Analysis of 2006 DFRWS forensic carving challenge - A smart carving approach].
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SmartCarving utilizes a combination of structure based validation along with validation of each file's unique content. Results for the SmartCarving technique
 
were demonstrated on fragmented jpegs in the DFRWS 2006 and DFRWS 2007 challenges. From these two challenges SmartCarving was able
 
were demonstrated on fragmented jpegs in the DFRWS 2006 and DFRWS 2007 challenges. From these two challenges SmartCarving was able
to recover all but one fragmented jpeg file.
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to recover all but one fragmented jpeg file.  
  
 
==History==
 
==History==
Memon et al.[1] presented an efficient algorithm based on a greedy heuristic and alpha-beta pruning for reassembling fragmented images.
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[[User:NasirMemon|Memon]] et al.[1] presented an efficient algorithm based on a greedy heuristic and alpha-beta pruning for reassembling fragmented images.
Building on this work, Memon et al.[2] researched and introduced sequential hypothesis testing as a an effective mechanism for detecting fragmentation points of file. This paper won the best paper award for DFRWS 2008. The techniques presented in the paper were the foundation for the overall SmartCarving design.
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Building on this work, [[User:NasirMemon|Memon]] et al.[2] researched and introduced sequential hypothesis testing as a an effective mechanism for detecting fragmentation points of file. This paper won the best paper award for DFRWS 2008. The techniques presented in the paper were the foundation for the overall SmartCarving design.
  
 
==Details==
 
==Details==
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==Applications==
 
==Applications==
There are currently two applications available that utilize SmartCarving, both produced by Digital Assembly:
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There are currently two commercial applications available that utilize SmartCarving, both produced by Digital Assembly:
* Adroit Photo Recovery
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* [[Adroit Photo Forensics]]
 
* [[Adroit Photo Forensics]]
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* Adroit Photo Recovery
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Further there is one open-source solution under development:
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* [https://github.com/rpoisel/mmc Multimedia File Carver] - Implementation that focuses on the recovery of fragmented movies and images (JPEG)
  
 
== References ==
 
== References ==

Latest revision as of 02:55, 25 September 2012

SmartCarving is a file carving technique to recover fragmented files first proposed by A. Pal, T. Sencar and N. Memon in DFRWS 2008. The term smart carving was already proposed in 2006 in Analysis of 2006 DFRWS forensic carving challenge - A smart carving approach.

SmartCarving utilizes a combination of structure based validation along with validation of each file's unique content. Results for the SmartCarving technique were demonstrated on fragmented jpegs in the DFRWS 2006 and DFRWS 2007 challenges. From these two challenges SmartCarving was able to recover all but one fragmented jpeg file.

History

Memon et al.[1] presented an efficient algorithm based on a greedy heuristic and alpha-beta pruning for reassembling fragmented images. Building on this work, Memon et al.[2] researched and introduced sequential hypothesis testing as a an effective mechanism for detecting fragmentation points of file. This paper won the best paper award for DFRWS 2008. The techniques presented in the paper were the foundation for the overall SmartCarving design.

Details

After identifying a header block of a specific file type, for example, jpeg, a SmartCarver will analyze each subsequent block to determine if it belongs or does not belong to the starting block. If a block is determined not to belong, then the file is assumed to be fragmented and the SmartCarving algorithm looks for the next fragment by matching the data of other available blocks with the first fragment. This process can be done in parallel for many files.

Applications

There are currently two commercial applications available that utilize SmartCarving, both produced by Digital Assembly:

Further there is one open-source solution under development:

References

External links