Curved Worlds, Clear Boundaries: Generalizing Speech Deepfake Detection using Hyperbolic and Spherical Geometry Spaces
Abstract
In this work, we address the challenge of generalizable audio deepfake detection (ADD) across diverse speech synthesis paradigms-including conventional text-to-speech (TTS) systems and modern diffusion or flow-matching (FM) based generators. Prior work has mostly targeted individual synthesis families and often fails to generalize across paradigms due to overfitting to generation-specific artifacts. We hypothesize that synthetic speech, irrespective of its generative origin, leaves behind shared structural distortions in the embedding space that can be aligned through geometry-aware modeling. To this end, we propose RHYME, a unified detection framework that fuses utterance-level embeddings from diverse pretrained speech encoders using non-Euclidean projections. RHYME maps representations into hyperbolic and spherical manifolds-where hyperbolic geometry excels at modeling hierarchical generator families, and spherical projections capture angular, energy-invariant cues such as periodic vocoder artifacts. The fused representation is obtained via Riemannian barycentric averaging, enabling synthesis-invariant alignment. RHYME outperforms individual PTMs and homogeneous fusion baselines, achieving top performance and setting new state-of-the-art in cross-paradigm ADD.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents RHYME, a geometry-aware framework for generalizable audio deepfake detection that effectively combines hyperbolic and spherical geometry to improve detection across diverse synthesis paradigms. This innovative approach addresses a critical gap in the field, offering a promising solution to enhance the robustness of deepfake detection systems.
Methodology
The proposed RHYME framework introduces a novel approach to audio deepfake detection by leveraging non-Euclidean geometry, specifically hyperbolic and spherical spaces, to model and fuse embeddings from various speech synthesis paradigms. This geometry-aware modeling is innovative as it addresses the challenge of generalization across different synthesis methods, which has been a significant limitation in prior work. The use of Riemannian barycentric averaging for fusion is a unique aspect that enhances the robustness of the detection framework.
Results & evaluation
The experiments conducted on two benchmark datasets (ASVspoof and DFADD) provide a rigorous evaluation of the RHYME framework's performance. The paper demonstrates strong results across various settings, including zero-shot and cross-corpus evaluations, which are critical for assessing generalization capabilities. However, the paper would benefit from more detailed statistical analysis and comparisons with a broader range of existing methods.
Reproducibility
The paper includes a GitHub repository link that provides access to the source code and models, which is a positive aspect for reproducibility. However, the paper lacks detailed implementation instructions and hyperparameter settings that would facilitate easier reproduction of results by other researchers.
Limitations
The study is limited to English-language datasets, which restricts the generalizability of the findings to multilingual and accented speech. Additionally, the paper does not address potential biases in the datasets used, which could affect the robustness of the detection framework in real-world applications.
Broader impact
The RHYME framework has significant implications for enhancing the security of audio communications against deepfake technologies, which are increasingly used for malicious purposes. By providing a reliable detection mechanism, the work contributes to the broader field of digital forensics and speech security. However, the authors acknowledge the potential for misuse of detection technologies, emphasizing the need for responsible deployment. The paper presents RHYME, a geometry-aware framework for generalizable audio deepfake detection that effectively combines hyperbolic and spherical geometry to improve detection across diverse synthesis paradigms. This innovative approach addresses a critical gap in the field, offering a promising solution to enhance the robustness of deepfake detection systems.
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