HCSI-Net: Hierarchical Cross-Stream Interaction for Generalizable Deepfake Detection

Deepfake detection is increasingly critical for multimedia forensics, yet many detectors degrade under distribution shifts caused by unseen generation pipelines, post-processing, and unconstrained capture conditions. To improve cross-dataset generalization, we propose a powerfull two-stream detector, named HCSI-Net, that couples a CNN and a transformer with progressive interaction during hierarchical feature extraction. The streams are linked via a novel bi-directional spatial crossgating mechanism that jointly refines local texture cues and global contextual information across stages. The model is trained using manipulation-agnostic supervision based on simulated forgery artifacts and evaluated under challenging cross-dataset evaluation scenarios. Experiments across six widely used datasets demonstrate robust generalization across diverse deepfake generation techniques, achieving a macro-average AUC of 89.13 and consistently outperforming a number of strong state-of-theart baselines. Ablation results confirm that intermediate crossstream interaction drives the observed gains. Code is available at https://github.com/markobrodaric/HCSI-Net.