Revisiting DIRE: towards universal AI-generated image detection.

Abstract

The rapid development of generative models has improved image quality and made image synthesis widely accessible, raising concerns about content credibility. To address this issue, we propose a method called Universal Reconstruction Residual Analysis (UR²EA) for detecting synthetic images. Our study reveals that, when GAN- and diffusion-generated images are reconstructed by pre-trained diffusion models, they exhibit significant differences in reconstruction error compared to real images: GAN-generated images show lower reconstruction quality than real images, whereas diffusion-generated images are more accurately reconstructed. We leverage these residual maps as a universal prior to training a model for detecting synthetic images. In addition, we introduce a Multi-scale Channel and Window Attention (MCWA) module to extract fine-grained features from residual maps across multiple scales, capturing both local and global details. To facilitate the exploration of diverse detection methods, we constructed a new UniversalForensics dataset, which includes various representations of synthetic images generated by 30 different models. Compared to the best-performing baselines, our method improves average accuracy by 3.3 % and precision by 1.6 %, achieving state-of-the-art results.