In recent years, generative large models have achieved remarkable progress, attracting widespread attention. With the rapid development of applications based on these models, public interest in creativity has significantly increased. Generated images have become prevalent in mainstream media and social networks, covering a wide range of topics and domains. Although these technologies have offered unprecedented opportunities for numerous industries, they also come with potential issues such as copyright infringement and information forgery. Existing models for detecting synthetic images typically suffer from low accuracy and weak generalization capabilities. To address these issues, we have proposed a novel method named DSFM. This method utilizes a combination of ResNet and Vision Transformer to simultaneously focus on shallow and deep information, thereby enhancing the overall performance of the model. Experiments conducted on four datasets-AGI, MMAF, Gide COCO, and SFHQ LSUN-demonstrate that our model significantly outperforms baseline models on the MMAF and Gide COCO datasets, with performance improvements nearing 30% on the MMAF dataset. Although improvements on the AGI dataset were modest, the model still displayed competitive performance. Under multiple evaluation metrics, this method has proven its excellent accuracy and superior generalization capabilities. The related datasets and code have been made publicly available and can be accessed at https://github.com/veinhao/ for further information.