Binary descriptors for Copy-Move Forgery Detection in Digital Photographs

Today, image forensic is an emerging area which aims at authenticating the credibility of an image. Sophisticating image editing tools make it easy to forge images in different ways and one amongst them is copy-move (CM) forgery which is considered in this paper. CM forgery modifies the content of an image by copying a portion of an image and pasting it in a distinct location in the similar image. Fraudsters, in order to conceal the fraud and to deceive the human eyes, sometimes do some post-processing operations such as rotation, scaling, multiple CM, etc. The widely used block-based methods for CM forgery detection are not robust enough to affine transformation and are not invariant to scaling, rotation, and noise. So, in this work, key-point-based CM forgery detection methods based on BRISK and ORB descriptors are proposed for detecting CM forgeries in digital images. The presented methods are dependent upon blobs, detecting using DoG operator, from which BRISK and ORB features are extracted. The extracted features are matched using Hamming distance metrics to find similar key points to identify the CM regions. The work was implemented in Python and synthesized images were used in this to analyze and compare the efficacy of the presented techniques. The experimental outcomes demonstrates that the presented technique was effectual for multi-CM attacks and geometric transformations namely rotation and scaling. Though both the methods were able to detect the CM forgeries efficiently, ORB executed faster compared to BRISK.