Tests an image for copy-move forgery (a portion of an image has been copied and then moved somewhere else within the image.) Written in Python 3. Requires skimage, numpy, and scipy. Written by Efron Licht, based off the algorithm "An efficient expanding block algorithm for image copy-move forgery detection" by Gavin Lynch, Frank Y. Shih, and Hong-Yuan Mark Liao, and published in Information Sciences 239 in 2013. Free for noncommerical use or modification, but please retain the above credits. Please ask for commerical use.

main function. ###input: filename that contains an mxn image (color or grayscale) known valid formats: '.png', '.jpg' ###output: imageConsideredModified, imgOut where imageConsideredModified is True | False if imageConsideredModified == True, imgOut is the original image if imageConsideredModified == False, imgOut is a (2m+8) x n x 3 image

##block_class class definitions and initilization.

a sub-block of a grayscale image

settings for 'efficient' image forgery detection, automatically determined by image size.

compares blocks at a pixel-to-pixel level to see if they are similar.

creates a masked image which lets the end-user visually see