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.

通过在音频中嵌入正交、不可逆的签名，SecureSpectra 防御机制应对 DeepFake 威胁，避免未经授权的访问和错误信息的传播。通过将差分隐私整合到流程中，保护签名免受逆向工程的威胁，在增强安全性和减少性能损失之间取得平衡。我们在 Mozilla Common Voice、LibriSpeech 和 VoxCeleb 数据集上的评估显示 SecureSpectra 具有卓越的性能，在检测准确度方面超过最近的研究成果多达 71%。我们将 SecureSpectra 开源，以造福研究界。