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Color Patterns And Enhanced Texture Learning For Detecting Computer-Generated Images.
- Source :
-
Computer Journal . Jun2024, Vol. 67 Issue 6, p2303-2316. 14p. - Publication Year :
- 2024
-
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]
- Subjects :
- *COMPUTER art
*DIGITAL images
*NEURAL circuitry
*COMPUTER algorithms
*BIG data
Subjects
Details
- Language :
- English
- ISSN :
- 00104620
- Volume :
- 67
- Issue :
- 6
- Database :
- Academic Search Index
- Journal :
- Computer Journal
- Publication Type :
- Academic Journal
- Accession number :
- 178338272
- Full Text :
- https://doi.org/10.1093/comjnl/bxae007