تقرير
SecureSpectra: Safeguarding Digital Identity from Deep Fake Threats via Intelligent Signatures
| العنوان: | SecureSpectra: Safeguarding Digital Identity from Deep Fake Threats via Intelligent Signatures |
|---|---|
| المؤلفون: | Baser, Oguzhan, Kale, Kaan, Chinchali, Sandeep P. |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Cryptography and Security, Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing |
| الوصف: | 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. Comment: 5 pages, 4 figures, Proc. INTERSPEECH 2024 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2407.00913 |
| رقم الأكسشن: | edsarx.2407.00913 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |