SecureSpectra: Safeguarding Digital Identity from Deep Fake Threats via Intelligent Signatures

TL;DR

SecureSpectra introduces an innovative audio signature embedding method that leverages high-frequency content differences to detect and prevent DeepFake audio attacks, enhancing voice authentication security.

Contribution

It presents a novel defense mechanism embedding irreversible signatures in audio, utilizing high-frequency content limitations of DeepFake models, with differential privacy for security and minimal performance impact.

Findings

Outperforms recent detection methods by up to 71% in accuracy

Effective across multiple datasets including Mozilla, LibriSpeech, VoxCeleb

Provides a practical, open-source solution for DeepFake audio detection

Abstract

Advancements in DeepFake (DF) audio models pose a significant threat to voice authentication systems, leading to unauthorized access and the spread of misinformation. We introduce a defense mechanism, SecureSpectra, addressing DF threats by embedding orthogonal, irreversible signatures within audio. SecureSpectra leverages the inability of DF models to replicate high-frequency content, which we empirically identify across diverse datasets and DF models. Integrating differential privacy into the pipeline protects signatures from reverse engineering and strikes a delicate balance between enhanced security and minimal performance compromises. Our evaluations on Mozilla Common Voice, LibriSpeech, and VoxCeleb datasets showcase SecureSpectra's superior performance, outperforming recent works by up to 71% in detection accuracy. We open-source SecureSpectra to benefit the research community.