Computer generated images vs. digital photographs: A synergetic feature and classifier combination approach

The development of powerful and low-cost hardware devices allied with great advances on content editing and authoring tools have promoted the creation of computer generated images (CG) to a degree of unrivaled realism. Differentiating a photo-realistic computer generated image from a real photograph (PG) can be a difficult task to naked eyes. Digital forensics techniques can play a significant role in this task. As a matter of fact, important research has been made by the scientific community in this regard. Most of the approaches focus on single image features aiming at detecting differences between real and computer generated images. However, with the current technology advances, there is no universal image characterization technique that completely solves this problem. In our work, we (1) present a complete study of several CG versus PG approaches; (2) create a large and heterogeneous dataset to be used as a training and validation database; (3) implement representative methods of the literature; and (4) devise automatic ways for combining the best approaches. We compared the implemented methods using the same validation environment showing their pros and cons with a common benchmark protocol. We collected approximately 4850 photographs and 4850 CGs with large diversity of image content and quality. We implemented a total of 13 methods. Results show that this set of methods can achieve up to 93% of accuracy when used without any form of machine learning fusion. The same methods, when combined through the implemented fusion schemes, can achieve an accuracy rate of 97%, representing a reduction of 57% of the classification error over the best individual result.