# Source Verification for Speech Deepfakes

arXiv: https://arxiv.org/abs/2505.14188 · HTML: https://mlsanity.com/paper/2505.14188/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2025.

## Built on by (most cited first, 5 of 5)

- [Disentangling Speaker Traits for Deepfake Source Verification via Chebyshev Polynomial and Riemannian Metric Learning](https://mlsanity.com/paper/2603.21875/) (2026)
- [Open-Set Source Tracing as Compositional Factors via Structured Prototypes](https://mlsanity.com/paper/2607.03134/) (2026)
- [Towards Generalized Source Tracing for Codec-Based Deepfake Speech](https://mlsanity.com/paper/2506.07294/) (2025)
- [Towards Reliable Audio Deepfake Attribution and Model Recognition: A Multi-Level Autoencoder-Based Framework](https://mlsanity.com/paper/2508.02521/) (2025)
- [SNAP: Speaker Nulling for Artifact Projection in Speech Deepfake Detection](https://mlsanity.com/paper/2603.20686/) (2026)

## Abstract (arXiv)

With the proliferation of speech deepfake generators, it becomes crucial not only to assess the authenticity of synthetic audio but also to trace its origin. While source attribution models attempt to address this challenge, they often struggle in open-set conditions against unseen generators. In this paper, we introduce the source verification task, which, inspired by speaker verification, determines whether a test track was produced using the same model as a set of reference signals. Our approach leverages embeddings from a classifier trained for source attribution, computing distance scores between tracks to assess whether they originate from the same source. We evaluate multiple models across diverse scenarios, analyzing the impact of speaker diversity, language mismatch, and post-processing operations. This work provides the first exploration of source verification, highlighting its potential and vulnerabilities, and offers insights for real-world forensic applications.