# Neural Codec-based Adversarial Sample Detection for Speaker Verification

arXiv: https://arxiv.org/abs/2406.04582 · HTML: https://mlsanity.com/paper/2406.04582/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2024.

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

- [DFADD: The Diffusion and Flow-Matching Based Audio Deepfake Dataset](https://mlsanity.com/paper/2409.08731/) (2024)
- [Towards Quantifying and Reducing Language Mismatch Effects in Cross-Lingual Speech Anti-Spoofing](https://mlsanity.com/paper/2409.08346/) (2024)
- [Impact of Phonetics on Speaker Identity in Adversarial Voice Attack](https://mlsanity.com/paper/2509.15437/) (2025)
- [Codec-Robust Attacks on Audio LLMs](https://mlsanity.com/paper/2605.20519/) (2026)
- [Towards Generalized Source Tracing for Codec-Based Deepfake Speech](https://mlsanity.com/paper/2506.07294/) (2025)

## Abstract (arXiv)

Automatic Speaker Verification (ASV), increasingly used in security-critical applications, faces vulnerabilities from rising adversarial attacks, with few effective defenses available. In this paper, we propose a neural codec-based adversarial sample detection method for ASV. The approach leverages the codec's ability to discard redundant perturbations and retain essential information. Specifically, we distinguish between genuine and adversarial samples by comparing ASV score differences between original and re-synthesized audio (by codec models). This comprehensive study explores all open-source neural codecs and their variant models for experiments. The Descript-audio-codec model stands out by delivering the highest detection rate among 15 neural codecs and surpassing seven prior state-of-the-art (SOTA) detection methods. Note that, our single-model method even outperforms a SOTA ensemble method by a large margin.