Learning the manifold of authenticity : hybrid-curvature representation learning for generalizable deepfake detection

Larue, Nicolas (author), Štruc, Vitomir (author), Peer, Peter (author), Vu, Ngoc-Son (author)

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.

• Language: English
• Keywords: contrast learning, generalized deepfake detection, hyperbolic, one-class anomaly detection
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FRI - Faculty of Computer and Information Science ; FE - Faculty of Electrical Engineering
• Publication status: Published
• Publication version: Version of Record
• Year: 2026
• Number of pages: Str. 1-14
• Numbering: Vol.
• PID: 20.500.12556/RUL-183410
• UDC: 004.932.2:004.8
• ISSN on article: 2169-3536
• DOI: 10.1109/ACCESS.2026.3702429
• COBISS.SI-ID: 281381123
• Publication date in RUL: 12.06.2026

Record is a part of a journal

• Title: IEEE access
• Publisher: Institute of Electrical and Electronics Engineers
• ISSN: 2169-3536
• COBISS.SI-ID: 519839513

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: kontrastno učenje, posplošena detekcija globokih ponaredkov, hiperboličnost, eno-razredna detekcija anomalij