Distinguishing Computer-Generated Images from Photographic Images: a Texture-Aware Deep Learning-Based Method

With the rapid development of computer graphics and generative models, computers are capable of generating images containing non-existent objects and scenes. Moreover, the computer-generated (CG) images may be indistinguishable from photographic (PG) images due to the strong representation ability of neural network and huge advancement of 3D rendering technologies. The abuse of such CG images may bring potential risks for personal property and social stability. Therefore, in this paper, we propose a dual-stream neural network to extract features enhanced by texture information to deal with the CG and PG image classification task. First, the input images are first converted to texture maps using the rotation-invariant uniform local binary patterns. Then we employ an attention-based texture-aware feature enhancement module to fuse the features extracted from each stage of the dual-stream neural network. Finally, the features are pooled and regressed into the predicted results by fully connected layers. The experimental results show that the proposed method achieves the best performance among all three popular CG and PG classification databases. The ablation study and cross-database validation experiments further confirm the effectiveness and generalization ability of the proposed algorithm.