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Distinguishing between computer-generated (CG) and natural photographic (PG)images is of great importance to verify the authenticity and originality ofdigital images. However, the recent cutting-edge generation methods enable highqualities of synthesis in CG images, which makes this challenging task eventrickier. To address this issue, a joint learning strategy with deep textureand high-frequency features for CG image detection is proposed. We firstformulate and deeply analyze the different acquisition processes of CG and PGimages. Based on the finding that multiple different modules in imageacquisition will lead to different sensitivity inconsistencies to theconvolutional neural network (CNN)-based rendering in images, we propose a deeptexture rendering module for texture difference enhancement and discriminativetexture representation. Specifically, the semantic segmentation map isgenerated to guide the affine transformation operation, which is used torecover the texture in different regions of the input image. Then, thecombination of the original image and the high-frequency components of theoriginal and rendered images are fed into a multi-branch neural networkequipped with attention mechanisms, which refines intermediate features andfacilitates trace exploration in spatial and channel dimensions respectively.Extensive experiments on two public datasets and a newly constructed datasetwith more realistic and diverse images show that the proposed approachoutperforms existing methods in the field by a clear margin. Besides, resultsalso demonstrate the detection robustness and generalization ability of theproposed approach to postprocessing operations and generative adversarialnetwork (GAN) generated images.

Joint Learning of Deep Texture and High-Frequency Features for Computer-Generated Image Detection