A quick fork and update to python3 for fun will gladly accept issues and or PRs

I'm only doing this as I saw it was a neglected project that didnt get any updates. If the original owner wants me to shutdown and merge back in please reach out to me.

imago-forensics 🕵️

Imago is a python tool that extract digital evidences from images recursively. This tool is useful throughout a digital forensic investigation. If you need to extract digital evidences and you have a lot of images, through this tool you will be able to compare them easily. Imago allows to extract the evidences into a CSV file or in a sqlite database. If in a JPEG exif are present GPS coordinates, Imago can extract the longitude and latitude and it can convert them to degrees and to retrieve relevant information like city, nation, zip code... Imago offers also the possibility to calculate Error Level Analysis, and to detect nudity these functionalities are in BETA.

Setup

Setup via pip

1. Install imago:

$ pip install imago

2. Once installed, one new binary should be available: :

$ imago 

And then it should output the imago's banner

Requirements:

python >= 3.6
exifread >= 2.1.2
python-magic >= 0.4.15
argparse >= 1.4.0
pillow >= 5.2.0
nudepy >= 0.4
imagehash >= 4.0
geopy >= 1.16.0

Usage

usage: imago.py [-h] -i INPUT [-x] [-g] [-e] [-n] [-d {md5,sha256,sha512,all}]
                [-p {ahash,phash,dhash,whash,all}] [-o OUTPUT] [-s]
                [-t {jpeg,tiff}]

optional arguments:
  -h, --help            show this help message and exit
  -i INPUT, --input INPUT
                        Input directory path
  -x, --exif            Extract exif metadata
  -g, --gps             Extract, parse and convert to coordinates, GPS exif
                        metadata from images (if any)It works only with JPEG.
  -e, --ela             Extract, Error Level Analysis image,It works only with
                        JPEG. *BETA*
  -n, --nude            Detect Nudity, It works only with JPEG, *BETA*
  -d {md5,sha256,sha512,all}, --digest {md5,sha256,sha512,all}
                        Calculate perceptual image hashing
  -p {ahash,phash,dhash,whash,all}, --percentualhash {ahash,phash,dhash,whash,all}
                        Calculate hash digest
  -o OUTPUT, --output OUTPUT
                        Output directory path
  -s, --sqli            Keep SQLite file after the computation
  -t {jpeg,tiff}, --type {jpeg,tiff}
                        Select the image, this flag can be JPEG or TIFF, if
                        this argument it is not provided, imago will process
                        all the image types(i.e. JPEG, TIFF)

The only required argument is -i which is the base directory from which imago will start to search for image file. You should also provide at least one type of extraction (i.e. exif, data, gps, digest).

Example:

$ imago -i /home/solvent/cases/c23/DCIM/ -o /home/solvent/cases/c23/ -x -s -t jpeg -d all

Where:

• -i path: is the base directory, where imago will search for file
• -o path: the output directory where imago will save the CSV file, with the extracted metadata
• -x : imago will extract EXIF metadata.
• -s: the temporary SQLite database will not be deleted after the processing.
• -t jpeg: imago will search only for jpeg images.
• -d all: imago will calculate md5, sha256, sha512 for the jpeg images.

Features:

Functionality	Status
Recursive directory navigation	✔️
file mtime (UTC)	✔️
file ctime (UTC)	✔️
file atime (UTC)	✔️
file size (bytes)	✔️
MIME type	✔️
Exif support	✔️
CSV export	✔️
Sqlite export	✔️
md5, sha256, sha512	✔️
Error Level Analysis	✔️ BETA
Full GPS support	✔️
Nudity detection	✔️ BETA
Perceptual Image Hashing	✔️
aHash	✔️
pHash	✔️
dHash	✔️
wHash	✔️

ToDo:

Task	Status
Test code	❌
Comments and Suggestions are welcome	👍

New Fork to do (red-ship-it)

• review libs being used
• test out code with all functions
• Restart export of csv
• look at testing structure
• integrate github actions
• Replace strings for better print mechanisim such as f-strings
• Look for new libs that may be nice to have

📑 Copyright and Licenses

Code copyright 2018 Redaelli. Code released under the MIT license.