Computer graphics identification combining convolutional and recurrent neural networks

Abstract

In this letter, a deep-learning-based pipeline is proposed to distinguish photographics (PGs) from computer-graphics (CGs) combining convolutional neural network (CNN) and recurrent neural network (RNN). In the preprocessing stage, the color space transformation and the Schmid filter bank are utilized to extract chrominance and luminance components, which suppress the irrelevant information of various image contents for the CG identification task. Then, a dual-path CNN architecture is designed to learn joint feature representations of local patches for exploiting their color and texture characteristics. To extract the global artifact, the directed acyclic graph RNN is applied to model the spatial dependence of local patterns. Finally, the output score of RNN is used to identify the input sample. The CG/PG dataset is constructed by collecting samples from the Internet. Experimental results show that the proposed framework can outperform state-of-The-Art methods on identification ability of CGs, especially for images with low resolution.