NPD-AFM: Lightweight Dual-Domain Method for Deepfake Detection

As generative models such as GANs and diffusion models continue to advance rapidly, the realism and diversity of forged images have increased substantially, presenting challenges to the reliability of visual content. Most existing detection methods rely on complex network architectures or large-scale training resources, making it difficult to balance detection performance and deployment efficiency. Moreover, their generalization capability across different generative model remains limited. In this work, we propose a lightweight deepfake detection method that integrates spatial and frequency domain artifact features. In detail, in the spatial domain, a Neighboring Pixel Difference (NPD) module is employed to captures local periodic structures introduced during upsampling. Second, the resulting representation is transformed into the frequency domain, where an Adaptive Frequency Mask (AFM) module performs learnable frequency selection to enhance discriminative features. Then, the processed frequency features are subsequently mapped back to the spatial domain and fed into a compact residual classifier for final discrimination. Experimental are conducted on four datasets involving 17 GAN models and 9 diffusion models. The results show that the proposed method attains an average accuracy of 90.27%. Comparative analyses reveal substantial performance gains over existing methods, while maintaining low computational complexity.