SecureSpectra: Safeguarding Digital Identity from Deep Fake Threats via Intelligent Signatures
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866917794089533440 |
|---|---|
| author | Baser, Oguzhan Kale, Kaan Chinchali, Sandeep P. |
| author_facet | Baser, Oguzhan Kale, Kaan Chinchali, Sandeep P. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_00913 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SecureSpectra: Safeguarding Digital Identity from Deep Fake Threats via Intelligent Signatures Baser, Oguzhan Kale, Kaan Chinchali, Sandeep P. Cryptography and Security Machine Learning Sound Audio and Speech Processing 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. |
| title | SecureSpectra: Safeguarding Digital Identity from Deep Fake Threats via Intelligent Signatures |
| topic | Cryptography and Security Machine Learning Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2407.00913 |