CloneShield: A Framework for Universal Perturbation Against Zero-Shot Voice Cloning

TL;DR

CloneShield is a universal adversarial perturbation framework that effectively protects against zero-shot voice cloning by degrading cloned speech quality while preserving the original audio perceptibility.

Contribution

We propose a novel multi-objective optimization approach with MGDA for robust, imperceptible adversarial perturbations against zero-shot voice cloning systems.

Findings

Significantly degrades speaker similarity in cloned speech.

Maintains high audio quality for protected inputs (PESQ=3.90).

Effective across multiple TTS systems and datasets.

Abstract

Recent breakthroughs in text-to-speech (TTS) voice cloning have raised serious privacy concerns, allowing highly accurate vocal identity replication from just a few seconds of reference audio, while retaining the speaker's vocal authenticity. In this paper, we introduce CloneShield, a universal time-domain adversarial perturbation framework specifically designed to defend against zero-shot voice cloning. Our method provides protection that is robust across speakers and utterances, without requiring any prior knowledge of the synthesized text. We formulate perturbation generation as a multi-objective optimization problem, and propose Multi-Gradient Descent Algorithm (MGDA) to ensure the robust protection across diverse utterances. To preserve natural auditory perception for users, we decompose the adversarial perturbation via Mel-spectrogram representations and fine-tune it for each sample. This design ensures imperceptibility while maintaining strong degradation effects on zero-shot cloned outputs. Experiments on three state-of-the-art zero-shot TTS systems, five benchmark datasets and evaluations from 60 human listeners demonstrate that our method preserves near-original audio quality in protected inputs (PESQ = 3.90, SRS = 0.93) while substantially degrading both speaker similarity and speech quality in cloned samples (PESQ = 1.07, SRS = 0.08).