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Proceedings of the AAAI Conference on Artificial Intelligence

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VoiceCloak: A Multi-Dimensional Defense Framework Against...
Qianyue Hu, · 2026-03-14 · via Proceedings of the AAAI Conference on Artificial Intelligence

Authors

  • Qianyue Hu SUN YAT-SEN UNIVERSITY
  • Junyan Wu SUN YAT-SEN UNIVERSITY
  • Wei Lu SUN YAT-SEN UNIVERSITY
  • Xiangyang Luo Information Engineering University

DOI:

https://doi.org/10.1609/aaai.v40i1.37002

Abstract

Diffusion Models (DMs) have achieved remarkable success in realistic voice cloning (VC), while they also increase the risk of malicious misuse. Existing proactive defenses designed for traditional VC models aim to disrupt the forgery process, but they have been proven incompatible with DMs due to the intricate generative mechanisms of diffusion. To bridge this gap, we introduce VoiceCloak, a multi-dimensional proactive defense framework with the goal of obfuscating speaker identity and degrading perceptual quality in potential unauthorized VC. To achieve these goals, we conduct a focused analysis to identify specific vulnerabilities within DMs, allowing VoiceCloak to disrupt the cloning process by introducing adversarial perturbations into the reference audio. Specifically, to obfuscate speaker identity, VoiceCloak first targets speaker identity by distorting representation learning embeddings to maximize identity variation, which is guided by auditory perception principles. Additionally, VoiceCloak disrupts crucial conditional guidance processes, particularly attention context, thereby preventing the alignment of vocal characteristics that are essential for achieving convincing cloning. Then, to address the second objective, VoiceCloak introduces score magnitude amplification to actively steer the reverse trajectory away from the generation of high-quality speech. Noise-guided semantic corruption is further employed to disrupt structural speech semantics captured by DMs, degrading output quality. Extensive experiments highlight VoiceCloak's outstanding defense success rate against unauthorized diffusion-based voice cloning. Additional audio samples of VoiceCloak are available in demo pages.

How to Cite

Hu, Q., Wu, J., Lu, W., & Luo, X. (2026). VoiceCloak: A Multi-Dimensional Defense Framework Against Unauthorized Diffusion-Based Voice Cloning. Proceedings of the AAAI Conference on Artificial Intelligence, 40(1), 399–407. https://doi.org/10.1609/aaai.v40i1.37002

Issue

Section

AAAI Technical Track on Application Domains I

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