SpecWav-Attack: Leveraging Spectrogram Resizing and Wav2Vec 2.0 for Attacking Anonymized Speech

2025/01/10 by Yuqi Li, Li, Yuqi, Yuanzhong Zheng +9 · 4 citations
Computer Science · #Hate Speech and Cyberbullying Detection

paper · pdf · doi:10.48550/arxiv.2505.09616

Abstract

This paper presents SpecWav-Attack, an adversarial model for detecting speakers in anonymized speech. It leverages Wav2Vec2 for feature extraction and incorporates spectrogram resizing and incremental training for improved performance. Evaluated on librispeech-dev and librispeech-test, SpecWav-Attack outperforms conventional attacks, revealing vulnerabilities in anonymized speech systems and emphasizing the need for stronger defenses, benchmarked against the ICASSP 2025 Attacker Challenge.

Cited by

• Measuring Soft Biometric Leakage in Speaker De-Identification Systems
• SegReConcat: A Data Augmentation Method for Voice Anonymization Attack
• Evaluating Identity Leakage in Speaker De-Identification Systems
• VoxATtack: A Multimodal Attack on Voice Anonymization Systems

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