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

--This paper presents SpecWav-Attack, an adversarial model for detecting speakers in anonymized speech. It leverages Wav2V ec2 for feature extraction [1] 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 [2]. This paper introduces SpecWav-Attack, a tailored adversarial model for attacking anonymized speech with a focus on Effective Equal Error Rate (EER). Using the ECAP A-TDNN architecture [3], we integrate the Wav2V ec2 self-supervised model [1] to enrich speech representations, enhancing sensitivity to variations in anonymized data.