Exposing Computer Generated Images by Eye's Region Classification via Transfer Learning of VGG19 CNN

The advance of computer graphics techniques comes revolutionizing games and movie's industries. Creating very realistic characters totally from computer graphics models is, nowadays, a reality. However, this advance comes with a big price: the realism of images is so big that it is difficult to realize when we are facing a computer generated image or a real photo. In this paper we propose a new approach for highly realistic computer generated images detection by exploring inconsistencies into the region of the eyes. Such inconsistencies are captured exploring the expression power of features extracted via transfer learning approach with VGG19 Deep Neural Network model. Unlike the state-of-the-art approaches, which looks to evaluate the entire image, proposed method focuses in specific regions (eyes) where computer graphics modeling still needs improvements. Experiments conducted over two different datasets containing extremely realistic images achieved an accuracy of 0.80 and an AUC of 0.88.