Hunting out graphic images from real images using recurrent neural network and extended principal color components

Gahye Lee, Seungkyu Lee

Abstract: With recent graphics technology creates surprisingly realistic contents, most of such artificial creatures help immersive virtual experience. On the other hand, still human can recognize whether an observed visual information is real or graphic model. In this work, we propose a deep learning based graphic and real image classification method to hunt out a graphic image from real images. In order to employ a deep learning approach, we have built graphic-real image data set consists of around 25K images. Quantitative classification and qualitative graphic image hunting results are presented that helps interesting applications such as fake image detection or image realism enhancement.