Deep forgery detection technologies are crucial for image and video recognition tasks, with their performance heavily reliant on the features extracted from both real and fake images. However, most existing methods primarily focus on spatial domain features, which limits their accuracy. To address this limitation, we propose an adaptive dual-domain feature representation method for enhanced deep forgery detection. Specifically, an adaptive region dynamic convolution module is established to efficiently extract facial features from the spatial domain. Then, we introduce an adaptive frequency dynamic filter to capture effective frequency domain features. By fusing both spatial and frequency domain features, our approach significantly improves the accuracy of classifying real and fake facial images. Finally, experimental results on three real-world datasets validate the effectiveness of our dual-domain feature representation method, which substantially improves classification precision.