CGNet: Detecting computer-generated images based on transfer learning with attention module

Abstract

The rapid development of digital image processing technology and the popularization of image editors have made computer-generated images more realistic so that it is more challenging to distinguish computer-generated images from natural images with the naked eye. Simultaneously, the malicious potential of highly realistic computer-generated images has made the detection of the authenticity of digital images a significant research area. In this work, we propose a computer-generated image detection algorithm through the application of transfer learning and Convolutional Block Attention Module. More specifically, our method uses the feature transfer module and feature fusion module to consider both the shallow content features and the deep semantic features of the image, thereby improving the accuracy of identifying computer-generated images. We validate our method through extensive experiments on various datasets, and the experimental results show that our model outperformed state-of-the-art methods and achieved an accuracy of 0.963. We also show that the proposed model has strong robustness and high generalization ability.