With the rapid development of artificial intelligence generation technology, the boundary between artificial intelligence (AI)-generated images and real images is becoming increasingly blurred, posing serious challenges to the credibility and authenticity of digital content. Addressing the insufficient generalization of existing AI-generated image detection methods in complex scenarios, this research proposes a Diffusion-Cross Attention Transformer (DCAT) framework for image authenticity verification. This framework innovatively combines diffusion model feature extractors and cross-attention vision transformers (ViT) to achieve fine-grained capture of image microscopic noise distribution and semantic relationships. Large-scale experimental validation was conducted on the GenImage dataset. The model demonstrated excellent performance in various degradation environments, with area under the receiver operating characteristic curve (AUC) remaining stable from 0.910 under no degradation conditions to 0.775 in extreme degradation environments, significantly outperforming traditional methods. The core contributions of this research include proposing a multi-scale noise analysis feature extraction method, constructing a cross-attention semantic association detection mechanism, and theoretically deepening the mathematical characterization of distribution differences between generated models and real images. This innovative approach not only provides key technological breakthroughs but also offers important technical support for maintaining the authenticity of digital content ecosystems, holding significant scientific and practical value for the field of artificial intelligence image generation and detection.
Li et al. (Fri,) studied this question.