# Securing Voice-driven Interfaces against Fake (Cloned) Audio Attacks

arXiv: https://arxiv.org/abs/1902.06782 · HTML: https://mlsanity.com/paper/1902.06782/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2019.

## Built on by (most cited first, 5 of 5)

- [Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forward](https://mlsanity.com/paper/2103.00484/) (2021)
- [EmoKnob: Enhance Voice Cloning with Fine-Grained Emotion Control](https://mlsanity.com/paper/2410.00316/) (2024)
- [Combining Automatic Speaker Verification and Prosody Analysis for Synthetic Speech Detection](https://mlsanity.com/paper/2210.17222/) (2022)
- [Frequency Domain-Based Detection of Generated Audio](https://mlsanity.com/paper/2205.01806/) (2022)
- [Faked Speech Detection with Zero Prior Knowledge](https://mlsanity.com/paper/2209.12573/) (2022)

## Abstract (arXiv)

Voice cloning technologies have found applications in a variety of areas ranging from personalized speech interfaces to advertisement, robotics, and so on. Existing voice cloning systems are capable of learning speaker characteristics and use trained models to synthesize a person's voice from only a few audio samples. Advances in cloned speech generation technologies are capable of generating perceptually indistinguishable speech from a bona-fide speech. These advances pose new security and privacy threats to voice-driven interfaces and speech-based access control systems. The state-of-the-art speech synthesis technologies use trained or tuned generative models for cloned speech generation. Trained generative models rely on linear operations, learned weights, and excitation source for cloned speech synthesis. These systems leave characteristic artifacts in the synthesized speech. Higher-order spectral analysis is used to capture differentiating attributes between bona-fide and cloned audios. Specifically, quadrature phase coupling (QPC) in the estimated bicoherence, Gaussianity test statistics, and linearity test statistics are used to capture generative model artifacts. Performance of the proposed method is evaluated on cloned audios generated using speaker adaptation- and speaker encoding-based approaches. Experimental results for a dataset consisting of 126 cloned speech and 8 bona-fide speech samples indicate that the proposed method is capable of detecting bona-fide and cloned audios with close to a perfect detection rate.