Abstract: With the rapid development of artificial intelligence technologies,images generated by models such as generative adversarial network (GAN) and diffusion model have reached a highly realistic level that it is difficult for the human eye to recognize the authenticity.Existing detection techniques show good performance under specific conditions,but their generalization abilities are usually unsatisfactory when facing images generated from unknown models and data.To address the above problems,this paper proposes a two-branch framework based on fine-grained local artifacts,which fully exploits the global spatial features of the image as well as the feature information extracted from multiple local regions.The artifacts caused by upsampling operations at the fine-grained level in the spatial domain,which are common in generative images,are exploited and combined with the global structural information of the image and the local detail information to enhance the generalization ability of the detection model in coping with different scenarios.With this strategy,the proposed method is able to analyze the image content more comprehensively and identify the unique fingerprints of the synthetic images,and shows stronger robustness and accuracy in identifying AI synthetic images.Experimental results show that the proposed method exhibits good performance when dealing with datasets generated by various GANs and diffusion models,further verifying the method’s excellent generalization ability.

Key words: generative adversarial nework(GAN), diffusion model, forgery detection, generative detection