Securing Voice Biometrics: One-shot Learning Approach For Audio Deepfake Detection

Abstract

The Automatic Speaker Verification (ASV) system is vulnerable to fraudulent activities using audio deepfakes, also known as logical-access voice spoofing attacks. These deepfakes pose a concerning threat to voice biometrics due to recent advancements in generative AI and speech synthesis technologies. While several deep learning models for speech synthesis detection have been developed, most of them show poor generalizability, especially when the attacks have different statistical distributions from the ones seen. Therefore, this paper presents Quick-SpoofNet, an approach for detecting both seen and unseen synthetic attacks in the ASV system using one-shot learning and metric learning techniques. By using the effective spectral feature set, the proposed method extracts compact and representative temporal embeddings from the voice samples and utilizes metric learning and triplet loss to assess the similarity index and distinguish different embeddings. The system effectively clusters sim

Stats

• citations15
• S2 citations—
• github stars0
• HF likes0
• heat score9.03
• arxiv keykhan2023securing

Related papers

• One-class Learning Towards Synthetic Voice Spoofing Detection (2020)17.31
• Automatic Speaker Verification Spoofing And Deepfake Detection Using Wav2vec 2.0 And Data Augmentation (2022)17.35
• Deep Residual Neural Networks For Audio Spoofing Detection (2019)0.00
• Continual Learning For Fake Audio Detection (2021)11.49
• Combining Automatic Speaker Verification And Prosody Analysis For Synthetic Speech Detection (2022)10.48
• Detection Of Doctored Speech: Towards An End-to-end Parametric Learn-able Filter Approach (2022)0.00
• Self-attention And Hybrid Features For Replay And Deep-fake Audio Detection (2024)0.00
• Toward Improving Synthetic Audio Spoofing Detection Robustness Via Meta-learning And Disentangled Training With Adversarial Examples (2024)6.77