An Evaluation of Patch Match-based Copy-Move Forgery Detection (CMFD) on Social Media Images

As society is ever more dependent on technology and digital media, the law depends more on digital forensics to find, keep, evaluate, and analyze digital evidence such as digital images and digital documents. The effective image editing application that constantly improves allows the user to change the image material or alter the image effortlessly. Copy-move forgery (CMF) is a very difficult form of forgery to detect. CMF involves copying part of an image and pasting it into one or more regions of the same image. However, the existing Copy-Move Forgery Detection (CMFD) method was only utilized on the existing image dataset, while social media images are on the common media today. In this paper, the PatchMatch-based CMFD method is evaluated with different platforms of social media images: Facebook, WhatsApp, and Twitter. The average performance generated by the PatchMatch-based CMFD method is 91% for the existing CMFD dataset. By replacing the dataset with the social media images dataset, the average performance slightly decreases to 84%.