2026 |
Larue, Nicolas; Štruc, Vitomir; Peer, Peter; Vu, Ngoc-Son Learning the Manifold of Authenticity: Hybrid-Curvature Representation Learning for Generalizable Deepfake Detection Journal Article In: IEEE Access, pp. 1–14, 2026, ISBN: 2169-3536. Abstract | Links | BibTeX | Tags: deep learning, deepfake, deepfake DAD, deepfake detection, hyperbolic learning, media forensics @article{AccessHyperbolic,The practical utility of deepfake detectors is crippled by a crisis of generalization: models that perform well on known manipulation techniques consistently fail when faced with unseen forgeries.We argue this failure stems from a fundamental geometric mismatch. Existing methods implicitly assume that the manifold of authentic faces can be modeled in a space of uniform curvature, typically Euclidean, which inade-quately captures the complex, multi-scale structure of facial features. This paper validates the hypothesis that authentic faces lie on a manifold whose geometry is inherently hybrid, requiring both angular compactness (a spherical property) and hierarchical organization (a hyperbolic property). To resolve this geometric mismatch, we introduce a novel detector, CTrue, that learns a unified, hybrid-curvature representation of facial authenticity. Trained exclusively on real faces via self-supervised learning, our method simultaneously projects facial embeddings onto two complementary manifolds: a hypersphere to enforce compactness and a hyperbolic space to model the natural feature hierarchy. A single set of mathematically-optimal prototypes acts as a ‘‘geometric bridge’’, unifying the learning objectives in both spaces. At inference, a composite score measures an embedding’s deviation from this learned manifold. On challenging cross-dataset and cross-manipulation benchmarks, our method achieves competitive generalization under a strictly pristine-only training setting, showing that hybrid-curvature representations provide an effective and data-efficient alternative for deepfake detection. |