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.
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
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