In recent years, generative adversarial networks have been widely used to generate realistic fake images, and the current research mainly focuses on deepfake detection in face images. However, we are now facing the emergence of fake satellite images, which could potentially mislead and threaten national security. To address this issue, we propose a hybrid network of convolutional neural network and Transformer for deepfake geographic image detection. Specifically, our method combines the local modeling capability of convolutional neural networks with the global modeling capability of transformers. This allows us to effectively extract both local features and global representations from the satellite images. Additionally, we enhance the detection performance of the model by introducing channel attention mechanisms. Experimental results demonstrate that our proposed method outperforms existing approaches. Furthermore, under common image processing scenarios, such as Joint Photographic Experts Group compression and Gaussian noise corruption, our method also performs better than other comparison methods.