MSF-DiffDet: A U-Shaped Spatial-Frequency Interactive Network for Diffusion-Generated Handwriting Deepfake Detection

Recent diffusion models can synthesize handwriting images with sufficient visual fidelity to challenge both human examiners and automated forensic systems, thereby undermining the reliability of document authentication. Existing spatial-domain detectors may fail to capture subtle generation artifacts embedded in complex stroke structures. To address this limitation, we propose MSF-DiffDet, a multi-scale spatial-frequency framework that jointly learns forensic cues from spatial and frequency representations. The framework employs Learnable Frequency-Aware Decomposition (LFAD) to extract frequency-aware features, Local Frequency Statistics (LFS) to characterize local spectral variations, MixBlock to enable hierarchical cross-domain interaction, and an Adaptive Fusion Skip-connection (AF-Skip) decoder to suppress less informative features during response-map generation. MSF-DiffDet is evaluated under intra-domain detection, image-degradation robustness, and zero-shot cross-generator settings across multiple handwriting generators. An additional joint-domain test with Chinese handwriting is included to examine behavior under simultaneous script, generator, and dataset shifts. It outperforms the representative baselines, achieving 93.49% accuracy, 96.30% AUC, and 10.45% EER in the intra-domain evaluation, while retaining 89.16% accuracy under zero-shot cross-generator evaluation on Test Set B. In a strict leave-one-generator-out (LOGO) setting, where the model is trained on DiffBrush and evaluated on WordStylist with writer-disjoint splits, MSF-DiffDet achieves the highest cross-generator AUC (84.74%) among the compared architectures, while the purely spatial and purely frequency-domain variants reach 81.68% and 77.90%, respectively. The gap between in-generator and cross-generator performance indicates that diffusion-generated handwriting detection remains partially generator-specific. A deletion-based faithfulness analysis further shows that high-response regions of the response map are functionally relevant to the image-level prediction. These results demonstrate the effectiveness of spatial-frequency feature interaction for diffusion-generated handwriting detection and for producing forgery-sensitive stroke-region response maps.