Takes files obtained by file-carving and extracts features from them to help prioritize "interesting" files.
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Takes files obtained by file-carving and extracts features from them to help prioritize "interesting" files.


Prioritize – Extracts data from all files from a supplied directory and stores them into a sqlite database.

python prioritize.py <path>

Examine – Reads the sqlite database and generates an HTML report.

python examine_results.py


One of the problems in digital forensics is dealing with the sheer amount of data that can be acquired from a system. The purpose of this project is to determine which files would likely be of most interest for a forensic investigator. A file is considered to be interesting if it has features that are characteristic of files that are useful during an investigation.

All of the collected data will be stored in a SQLite database for easy access afterwards. To facilitate the use of the collected data, examine_data.py will generate an HTML file that includes the images in that order.

This program will initially only support image files. For an image file, the following data is considered interesting:

  • Screenshots of a user's desktop
  • Pictures of people
  • Pictures of credit cards
  • EXIF location, date, and camera model information

As such, the following features will be extracted:

  • Removing 'useless' images:
  • Check if the image is well-structured, and can be opened in a normal image viewer. If it is not, there is no point in examining it further.
  • Check the variation of the colors within the image, to rule out images that are a solid color.
  • Data collection:
  • The number of faces within the image
  • If the image looks like it may be a screen capture (based on the presence of artifacts that are usually on a desktop, like a start menu, or icons for well-known programs)
  • If the image contains a credit card
  • If the image contains something that looks like a photo ID
  • The presence of EXIF data, and specific EXIF fields (GPS, date, and camera model)


This script requires:

  • python 2.7,
  • Python imaging library
  • Python opencv 2.4.4
  • numpy

To install these dependencies on Ubuntu, run: apt-get install python-opencv python-numpy Then install opencv 2.4.4 from http://opencv.org/downloads.html