An Overview of Copy Move Forgery Detection Approaches

Abstract

Images have greater expressive power than any other forms of documents. With the Internet, images are widespread in several applications. But the availability of efficient open-source online photo editing tools has made editing these images easy. The fake images look more appealing and original than the real image itself, which makes them indistinguishable and hence difficult to detect. The authenticity of digital images like medical reports, scan images, financial data, crime evidence, legal evidence, etc. is of high importance. Detecting the forgery of images is therefore a major research area. Image forgery is categorized as copy-move forgery, splicing, and retouching. In this work, a review of copy-move forgery is discussed along with the existing research on its detection and localization using both conventional and deep-learning mechanisms. The datasets used and challenges towards improving or developing novel algorithms are also presented.