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.

翻译：DeepFake（DF）音频模型的进步对语音认证系统构成重大威胁，导致未经授权的访问和错误信息的传播。我们提出一种防御机制SecureSpectra，通过在音频中嵌入正交且不可逆的签名来应对DF威胁。SecureSpectra利用DF模型无法复制高频内容的特点，这一现象我们在多个数据集和DF模型上进行了实证验证。在流程中集成差分隐私技术可保护签名免遭逆向工程，并在增强安全性与最小化性能损失之间实现微妙平衡。我们在Mozilla Common Voice、LibriSpeech和VoxCeleb数据集上的评估表明，SecureSpectra具有卓越性能，其检测准确率最高超过近期同类方法71%。我们将开源SecureSpectra以造福研究社区。