# Attacking Voice Anonymization Systems with Augmented Feature and Speaker Identity Difference

arXiv: https://arxiv.org/abs/2412.19068 · HTML: https://mlsanity.com/paper/2412.19068/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Dec 2024.

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

- [You Are What You Say: Exploiting Linguistic Content for VoicePrivacy Attacks](https://mlsanity.com/paper/2506.09521/) (2025)
- [SegReConcat: A Data Augmentation Method for Voice Anonymization Attack](https://mlsanity.com/paper/2508.18907/) (2025)
- [DAST: A Dual-Stream Voice Anonymization Attacker with Staged Training](https://mlsanity.com/paper/2603.12840/) (2026)
- [Evaluating Identity Leakage in Speaker De-Identification Systems](https://mlsanity.com/paper/2508.14012/) (2025)
- [Beyond EER: Multi-Dimensional Evaluation of Information Leakage in Speaker De-Identification](https://mlsanity.com/paper/2609.18673/) (2026)

## Abstract (arXiv)

This study focuses on the First VoicePrivacy Attacker Challenge within the ICASSP 2025 Signal Processing Grand Challenge, which aims to develop speaker verification systems capable of determining whether two anonymized speech signals are from the same speaker. However, differences between feature distributions of original and anonymized speech complicate this task. To address this challenge, we propose an attacker system that combines Data Augmentation enhanced feature representation and Speaker Identity Difference enhanced classifier to improve verification performance, termed DA-SID. Specifically, data augmentation strategies (i.e., data fusion and SpecAugment) are utilized to mitigate feature distribution gaps, while probabilistic linear discriminant analysis (PLDA) is employed to further enhance speaker identity difference. Our system significantly outperforms the baseline, demonstrating exceptional effectiveness and robustness against various voice anonymization systems, ultimately securing a top-5 ranking in the challenge.