通过增强特征与身份差异，突破语音匿名系统的验证防御
Attacking Voice Anonymization Systems with Augmented Feature and Speaker Identity Difference
- 融合数据增强与身份差异建模，提升匿名语音验证能力
- 在多个匿名系统下表现优异，显著超越基线模型
- 适合研究语音隐私与安全验证的学者和工程师
本研究针对 ICASSP 2025 信号处理大赛中的首个语音隐私攻击挑战，致力于构建能够判断两段匿名语音是否来自同一说话人的声纹验证系统。由于原始语音与匿名化语音之间特征分布存在差异，该任务极具挑战性。为此，我们提出一种结合数据增强特征表示与说话人身份差异增强分类器的攻击系统（DA-SID）。具体而言，采用数据融合与SpecAugment等数据增强策略以缩小特征分布差距，同时利用概率线性判别分析（PLDA）进一步强化说话人身份差异。实验表明，该系统在多种语音匿名化方案下均显著优于基线，展现出卓越的性能与鲁棒性，最终在挑战赛中取得前五名成绩。
原文摘要 · Abstract (English)
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.
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