SecureSpectra: Safeguarding Digital Identity from Deep Fake Threats via Intelligent Signatures
This addresses security vulnerabilities in digital identity systems for users and organizations, though it is an incremental improvement on existing signature-based defenses.
The paper tackles the threat of DeepFake audio models to voice authentication by introducing SecureSpectra, a defense mechanism that embeds irreversible signatures in audio, achieving up to 71% higher detection accuracy than recent works.
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.