# SyntheticPop: Attacking Speaker Verification Systems With Synthetic VoicePops

arXiv: https://arxiv.org/abs/2502.09553 · HTML: https://mlsanity.com/paper/2502.09553/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 13 Feb 2025.

## Builds on (most cited first, 4 of 4)

- [Learning from Noisy Labels with Deep Neural Networks: A Survey](https://mlsanity.com/paper/2007.08199/) (2020)
- [A Survey on Audio Diffusion Models: Text To Speech Synthesis and Enhancement in Generative AI](https://mlsanity.com/paper/2303.13336/) (2023)
- [Who is Real Bob? Adversarial Attacks on Speaker Recognition Systems](https://mlsanity.com/paper/1911.01840/) (2019)
- [Poisoning Attacks and Defenses on Artificial Intelligence: A Survey](https://mlsanity.com/paper/2202.10276/) (2022)

## Built on by (most cited first, 2 of 2)

- [A Survey of Threats Against Voice Authentication and Anti-Spoofing Systems](https://mlsanity.com/paper/2508.16843/) (2025)
- [VocalBridge: Latent Diffusion-Bridge Purification for Defeating Perturbation-Based Voiceprint Defenses](https://mlsanity.com/paper/2601.02444/) (2026)

## Abstract (arXiv)

Voice Authentication (VA), also known as Automatic Speaker Verification (ASV), is a widely adopted authentication method, particularly in automated systems like banking services, where it serves as a secondary layer of user authentication. Despite its popularity, VA systems are vulnerable to various attacks, including replay, impersonation, and the emerging threat of deepfake audio that mimics the voice of legitimate users. To mitigate these risks, several defense mechanisms have been proposed. One such solution, Voice Pops, aims to distinguish an individual's unique phoneme pronunciations during the enrollment process. While promising, the effectiveness of VA+VoicePop against a broader range of attacks, particularly logical or adversarial attacks, remains insufficiently explored. We propose a novel attack method, which we refer to as SyntheticPop, designed to target the phoneme recognition capabilities of the VA+VoicePop system. The SyntheticPop attack involves embedding synthetic "pop" noises into spoofed audio samples, significantly degrading the model's performance. We achieve an attack success rate of over 95% while poisoning 20% of the training dataset. Our experiments demonstrate that VA+VoicePop achieves 69% accuracy under normal conditions, 37% accuracy when subjected to a baseline label flipping attack, and just 14% accuracy under our proposed SyntheticPop attack, emphasizing the effectiveness of our method.