1. Color Patterns And Enhanced Texture Learning For Detecting Computer-Generated Images.
- Author
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Xu, Qiang, Xu, Dongmei, Wang, Hao, Yuan, Jianye, and Wang, Zhe
- Subjects
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COMPUTER art , *DIGITAL images , *NEURAL circuitry , *COMPUTER algorithms , *BIG data - Abstract
Detection of computer-generated (CG) images can reveal the authenticity and originality of digital images. However, recent cutting-edge image generation methods make it very difficult to distinguish CG images from natural photographs. In this paper, a novel method based on color patterns and enhanced texture learning is proposed to tackle this problem. We designed and implemented the backbone network with a separation-fusion learning strategy by constructing a multi-branch neural network. The luminance and chrominance patterns in dual-color spaces (RGB and YCbCr) are leveraged to achieve a robust representation of image differences. A channel-spatial attention module and a global texture enhancement module are also integrated into a backbone network to enhance the learning of inherent traces. Experiments on several commonly used benchmark datasets and a newly constructed dataset with more realistic and diverse images demonstrate that the proposed algorithm outperforms state-of-the-art competitors by a large margin. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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