With the rapid advancement of smart glasses, voice interaction has become widely deployed due to its naturalness and convenience. However, its practicality is often undermined by the vulnerability to spoofing attacks and interference from surrounding sounds, making seamless voice authentication crucial for smart glasses usage. To address this challenge, we propose AuthGlass, a voice authentication approach that leverages both air- and bone-conducted speech features to enhance accuracy and liveness detection. Aiming to gain comprehensive knowledge on speech-related acoustic and vibration features, we built a smart glasses prototype with redundant synchronized microphones: 14 air-conductive microphones and 2 bone-conductive units. In a study with 42 participants, we validated that combining sound-field and vibration features significantly improves authentication robustness and attack resistance. Furthermore, experiments demonstrated that AuthGlass maintains competitive accuracy even under various practical scenarios, highlighting its applicability and scalability for real-world deployment.

翻译：随着智能眼镜的快速发展，语音交互因其自然性与便捷性已得到广泛应用。然而，其实际应用常因易受欺骗攻击及环境声音干扰而受限，这使得无缝语音认证成为智能眼镜使用的关键。为应对这一挑战，我们提出AuthGlass——一种融合空气传导与骨骼传导语音特征以提升准确性与活体检测能力的语音认证方法。为全面获取语音相关的声学与振动特征，我们构建了配备冗余同步麦克风的智能眼镜原型：包含14个空气传导麦克风与2个骨骼传导单元。通过对42名参与者的研究，我们验证了结合声场特征与振动特征能显著提升认证鲁棒性与抗攻击能力。此外，实验表明AuthGlass在多种实际场景下仍能保持优越的准确率，凸显了其在真实场景中部署的适用性与可扩展性。