Forensic is an image processing library which aims to detect copy-move forgeries in digital images. The implementation is mainly based on this paper: https://arxiv.org/pdf/1308.5661.pdf

• Convert the RGB image to YUV color space.
• Divide the R,G,B,Y components into fixed-sized blocks.
• Obtain each block R,G,B and Y components.
• Calculate each block R,G,B and Y components DCT (Discrete Cosine Transform) coefficients.
• Extract features from the obtained DCT coefficients and save it into a matrix. The matrix rows will contain the blocks top-left coordinate position plus the DCT coefficient. The matrix will have (M − b + 1)(N − b + 1)x9 elements.
• Sort the features in lexicographic order.
• Search for similar pairs of blocks. Because identical blocks are most probably neighbors, after ordering them in lexicographic order we need to apply a specific threshold to filter out the false positive detections. If the distance between two neighboring blocks is smaller than a predefined threshold the blocks are considered as a pair of candidate for the forgery.
• For each pair of candidate compute the cumulative number of shift vectors (how many times the same block is detected). If that number is greater than a predefined threshold the corresponding regions are considered forged.

First install Go if you haven't installed already, set your GOPATH, and make sure $GOPATH/bin is on your PATH.

$ export GOPATH="$HOME/go"
$ export PATH="$PATH:$GOPATH/bin"

Next download the project and build the binary file.

$ go get -u -f github.com/esimov/forensic
$ go install

In case you do not want to build the binary file yourself you can obtain the prebuilt one from the releases folder.

$ forensic -in input.jpg -out output.jpg

$ forensic --help

Image forgery detection library.
    Version: 

  -blur int
    	Blur radius (default 1)
  -bs int
    	Block size (default 4)
  -dt float
    	Distance threshold (default 0.4)
  -ft float
    	Forgery threshold (default 210)
  -in string
    	Input image
  -ot int
    	Offset threshold (default 72)
  -out string
    	Output image

Original image	Forged image	Detection result

The library sometimes produce false positive detection, depending on the image content. For this reason i advice to adjust the settings. Also sometimes the human judgement is required, but in the most cases the library do a pretty good job in detecting forged images. The more intensive the overlayed color is, the more certain is that the image is tampered.

This project is under the MIT License. See the LICENSE file for the full license text.