Improving the Generalization and Robustness of Computer-Generated Image Detection Based on Contrastive Learning

With the rapid development of image generation techniques, it becomes much more difficult to distinguish high-quality computer-generated (CG) images from photographic (PG) images, challenging the authenticity and credibility of digital images. Therefore, distinguishing CG images from PG images has become an important research problem in image forensics, and it is crucial to develop reliable methods to detect CG images in practical scenarios. In this paper, we propose a forensics contrastive learning (FCL) framework to adaptively learn intrinsic forensics features for the general and robust detection of CG images. The data augmentation module is specially designed for CG image forensics, which reduces the interference of forensic-irrelevant information and enhances discrimination features between CG and PG images in both the spatial and frequency domains. Instance-wise contrastive loss and patch-wise contrastive loss are simultaneously applied to capture critical discrepancies between CG and PG images from global and local views. Extensive experiments on different public datasets and common post-processing operations demonstrate our approach can achieve significantly better generalization and robustness than the state-of-the-art approaches.