HyperDetect: Hierarchy-Aware Representation Learning for Generalizable Deepfake Detection

Generalizable deepfake detection requires representations that transfer across both manipulation techniques and visual domains. Existing detectors often learn generator-specific appearance cues while overlooking the hierarchical relationships among forgery families and the style changes induced by domain shifts. We introduce HyperDetect, a vision-language framework that represents forgery hierarchies in Lorentz hyperbolic space. Its learning objective combines hyperbolic angular contrastive learning with partial-order constraints, hierarchical entailment loss, and domain dispersion regularization. Together, these components exploit negative curvature to organize related forgery types, learn discriminative subtype representations, and address domain variation and class imbalance. Evaluations on multiple deepfake benchmarks demonstrate improved generalization to unseen manipulations compared with existing methods. The results highlight the value of explicitly modeling forgery hierarchies for transferable multimedia forensics. Code and data will be released publicly.