A study on detection of copy-move forgery in digital images

Abstract

With the recent elevation in social networking services like Facebook and Instagram, there has been a rise in the abundance use of image data. Trending image processing software's have an access to create doctored images which is an utmost perturb for the society at large. Fake stories are emanated from these images and are habitually used in malicious ways. Hence, there is a need for a genuine authentication for images. Image forgery is carried out conveniently but analyzing image manipulation is a great concern. Copy-move forgery is a technique that copy's a content from an image and pastes it in the same image to make it unauthentic or to hide the meaningful information. In image manipulation, Copy-move forgery can be performed easily and effectively, especially when source and target regions both are from the same image and share the same properties such as color, illumination conditions and noise. This paper elaborates survey on various Copy-Move Forgery Detection (CMFD) approaches. It is categorized into block based and keypoint based methods. Copy-move forgery is easy to perform and can be relatively effective in image manipulation, particularly when both source and target regions are from the same image as properties such as color temperature, illumination conditions and noise will generally be well-matched between the tampered region and the image.