The rapid advancement of Generative Adversarial Networks (GANs), particularly StyleGAN, has led to an unprecedented increase in highly realistic synthetic images. While this technology opens up exciting opportunities across various fields, it poses significant challenges to digital security and content authenticity. To address this issue, our study focuses on developing a robust method for detecting facial images generated by StyleGAN. We propose an optimized Vision Transformers (ViT) model that leverages transfer learning and incorporates a latent attention module. This approach enhances the model’s detection capabilities, effectively identifying StyleGAN-generated images. A comprehensive evaluation, which includes large datasets of real and generated images, demonstrates the model’s remarkable performance. The proposed model achieves an accuracy of 99.83%, an AUC of 1, and an F1-score of 0.9983. Furthermore, the model exhibits strong generalization abilities on external datasets, confirming its efficacy in various deepfake detection scenarios. Furthermore, due to the late attention integration, the computational cost can be reduced by 42%, achieving an 85% reduction for a specific dataset. We extensively validated our approach on three diverse StyleGAN-generated deepfake datasets and compared its performance to six baseline methods, demonstrating its superiority in detecting StyleGAN-generated deepfakes and its contribution to digital content authentication and synthetic image detection.