Methods to distinguish photorealistic computer generated images from photographic images: A review

Abstract

Uses of digital images have increased multifold in last few years in various important fields such as virtual reality, gaming, social media, magazine, news papers, medical, legal issues, law, academics etc. At the same time, image editing and rendering tools have also evolved significantly. With the help of computers and such advanced image rendering tools it is possible to create photorealistic computer graphics images effortlessly. It is very difficult to discriminate such photorealistic computer graphics images from actual photographic images taken from digital cameras by human visual system. If computer generated images are used with malicious intentions it creates negative impact on the society. Therefore, several methods have been proposed in last few years to distinguish computer generated images from photographic images. This paper presents a comprehensive review of the existing methods. A classification of all existing methods is also provided based on the use of feature extraction techniques and classifier used. Accordingly, all the existing methods are grouped into four categories: statistical feature based, acquisition process based, visual feature based, and hybrid feature based methods. This paper also reviews publically available related image datasets and suggests the future directions.