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The Automatic Speaker Verification (ASV) system is vulnerable to fraudulentactivities using audio deepfakes, also known as logical-access voice spoofingattacks. These deepfakes pose a concerning threat to voice biometrics due torecent advancements in generative AI and speech synthesis technologies. Whileseveral deep learning models for speech synthesis detection have beendeveloped, most of them show poor generalizability, especially when the attackshave different statistical distributions from the ones seen. Therefore, thispaper presents Quick-SpoofNet, an approach for detecting both seen and unseensynthetic attacks in the ASV system using one-shot learning and metric learningtechniques. By using the effective spectral feature set, the proposed methodextracts compact and representative temporal embeddings from the voice samplesand utilizes metric learning and triplet loss to assess the similarity indexand distinguish different embeddings. The system effectively clusters similarspeech embeddings, classifying bona fide speeches as the target class andidentifying other clusters as spoofing attacks. The proposed system isevaluated using the ASVspoof 2019 logical access (LA) dataset and testedagainst unseen deepfake attacks from the ASVspoof 2021 dataset. Additionally,its generalization ability towards unseen bona fide speech is assessed usingspeech data from the VSDC dataset.

Securing Voice Biometrics: One-Shot Learning Approach for Audio Deepfake Detection