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arXiv · 2607.11538

Evidence Subspace Projection: Measuring How Much Evidence Explains Deepfake Detection in Self-Supervised Speech Models

Yixuan Xiao·Cheng-Wei Lin·Xin Wang·Yassine El Kheir·Arnab Das·Tim Polzehl·Sebastian Möller·Ngoc Thang Vu
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Abstract

Self-supervised learning (SSL) models are widely used as feature extractors for state-of-the-art audio deepfake detection, but it remains unclear how to directly and quantitatively connect what SSL models capture to detection decisions. To address this gap, we propose Evidence Subspace Projection, a method that represents both evidence factors (e.g., attack category, codec, gender, transmission) and authenticity labels in a shared space constructed from SSL models' neuron activation patterns. By projecting the decision vector onto each evidence subspace, we obtain a scalar ratio that quantifies the explanatory power of each evidence type. We evaluate SSL models in raw, fine-tuned, and post-trained settings on multiple datasets. The results confirm findings from established studies, validating the proposed method, and reveal new insights into model behavior.

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Yixuan Xiao, Cheng-Wei Lin, Xin Wang, Yassine El Kheir, Arnab Das, Tim Polzehl, Sebastian Möller, Ngoc Thang Vu. 2026-07-13. Evidence Subspace Projection: Measuring How Much Evidence Explains Deepfake Detection in Self-Supervised Speech Models. https://arxiv.org/abs/2607.11538

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