HiDD-Net: a hierarchical dual-domain distillation network for efficient deepfake detection
The rapid advancement of generative AI has made high-quality deepfakes increasingly accessible, raising serious concerns regarding misinformation and digital trust. Consequently, developing reliable deepfake detection systems has become a critical task in multimedia forensics. However, existing methods often face a severe trade-off between cross-dataset generalization and computational efficiency. They heavily rely on large-scale backbones, which limits their deployment on resource-constrained edge devices. To bridge this gap, we propose HiDD-Net, a hierarchical dual-domain distillation framework. Specifically, the teacher network integrates multi-granularity spatial views (global, local, and facial) via a cross-attention mechanism to explicitly align fine-grained anomalies with global context. Simultaneously, it captures subtle upsampling spectral artifacts using a three-level discrete wavelet transform (DWT) and Spatial Rich Model (SRM) features. Subsequently, we distill these fused spatial-frequency representations into a compact student model (MobileNetV3) by enforcing feature alignment in a shared semantic space. Extensive evaluations on DeepfakeBench demonstrate that the student achieves a competitive cross-dataset average AUC of 0.771 (vs. the teacher’s 0.796) with only 6.67M parameters. Furthermore, it supports real-time CPU inference (30+ FPS) and remains highly robust to JPEG compression and Gaussian blur, offering a practical solution for mobile forensic deployment.