Audio-Driven Adversarial Defense for 3D Talking Face Generation with totally Visual Fidelity Preservation

2026-08-31

AI Summary

为解决音频驱动的3D人脸生成导致的身份冒用问题，本文通过在语音信号中加入心理声学掩蔽的微扰来保护隐私，同时保持视觉质量。

Abstract

The rapid development of generative portrait models has raised growing concerns about privacy leakage and identity misuse. In particular, audio-driven 3D talking face generation can reconstruct a reusable 3D portrait of a target person from a monocular video and animate it with arbitrary speech, making realistic identity impersonation alarmingly practical. Existing proactive defenses mainly operate in the visual domain by injecting subtle perturbations into acial regions to disrupt identity acquisition. However, such perturbations often compromise visual quality due to the strong structural priors and social sensitivity of human faces, and are easily weakened by common real-world transformations such as resizing. To overcome these limitations, we propose an imperceptible audio defense for audio-driven 3D talking face generation by shifting protection from the visual modality to the audio modality. Specifically,we exploit psychoacoustic masking to hide protective perturbations within perceptually masked frequency regions of the speech signal, thereby reducing perceptual distortion while suppressing reliable facial animation. Extensive experiments demonstrate that the proposed method effectively degrades 3D talking face generation while preserving favorable perceptual quality. These findings highlight psychoacoustically guided audio perturbations as a practical and promising direction for privacy-preserving portrait protection.

Problem

• audio-driven
• 3D talking face generation
• privacy leakage
• identity misuse
• visual quality

Innovation

• audio-driven
• psychoacoustic masking
• privacy-preserving