Affiliation:
1. Department of Electronics Engineering, Ramrao Adik Institute of Technology, DY Patil deemed to be University Nerul, Navi Mumbai, Maharashtra 400706, India
2. Department of Electronics & Telecommunication Engineering, Ramrao Adik Institute of Technology, DY Patil deemed to be University, Nerul, Navi Mumbai, Maharashtra 400706, India
Abstract
The advent in graphic rendering software and technological progress in hardware can generate or modify photorealistic computer graphic (CG) images that are difficult to identify by human observers. Computer-generated images are used in magazines, film and advertisement industry, medical and insurance agencies, social media, and law agencies as an information carrier. The forged computer-generated image created by the malicious user may distort social stability and impacts on public opinion. Hence, the precise identification of computer graphic and photographic image (PG) is a significant and challenging task. In the last two decades, several researchers have proposed different algorithms with impressive accuracy rate, including a recent addition of deep learning methods. This comprehensive survey presents techniques dealing with CG and PG image classification using machine learning and deep learning. In the beginning, broad classification of all the methods in to five categories is discussed in addition to generalized framework of CG detection. Subsequently, all the significant works are surveyed and are grouped into five types: image statistics methods, acquisition device properties-based techniques, color, texture, and geometry-based methods, hybrid methods, and deep learning methods. The advantages and limitations of CG detection methods are also presented. Finally, major challenges and future trends in the CG and PG image identification field are discussed.
Publisher
World Scientific Pub Co Pte Ltd
Subject
Computer Graphics and Computer-Aided Design,Computer Science Applications,Computer Vision and Pattern Recognition