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Source code for fingerprint recognition, face recognition and much more

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With the rapid development of computer technology, crime involving digital evidence is becoming more commonplace. Digital forensics involves an investigation of digital evidence to enable investigators to determine the truth about what happened. However, to achieve this goal, law enforcement must have tools and technologies that enable them to examine the evidence accurately. Unfortunately, as computer technology has advanced, criminals have found a myriad of ways to avoid law enforcement detection. The forensics community seems to be ill prepared for these anti-forensic techniques. In fact, most of the discussion about these methods is taking place outside the law enforcement community.

A criminal might attempt to hide an image by changing its extension to “.doc” so it appears to be a Microsoft Word document. Some forensics software looks at a file’s contents to determine what information it contains. This allows investigators to detect the type of the contents even if the file extension has been changed. However, the software currently detects images based on hard coded file signatures. The first few bytes of the file are examined to determine what type of information the file contains. This method of detecting file types works well in cases where the file has been otherwise unaltered; but it may fail when the file’s contents do not match the predetermined signatures. The signature-based method of detecting file types leaves the software susceptible to “evidence counterfeiting.” Evidence counterfeiting manipulates existing evidence to hide its purpose or creates new evidence that is deceptive. If a suspect alters any of the first few bytes of a file, the forensics packages are no longer able to detect what type of information is in the file.

We have developed a simple and efficient algorithm for file type identification that combines first order byte frequency analysis and second order statistics of byte distribution.

Index Terms: Matlab, source, code, file type, recognition, identification, extension, forensic.





Figure 1. File types

A simple and effective source code for File Type Recognition System.

Major features


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The authors have no relationship or partnership with The Mathworks. All the code provided is written in Matlab language (M-files and/or M-functions), with no dll or other protected parts of code (P-files or executables). The code was developed with Matlab 2006a. Matlab is required. The code provided has to be considered "as is" and it is without any kind of warranty. The authors deny any kind of warranty concerning the code as well as any kind of responsibility for problems and damages which may be caused by the use of the code itself including all parts of the source code.

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