Abstract
Photorealistic talking-head avatars are becoming increasingly common in virtual meetings, gaming, and social platforms. These avatars allow for more immersive communication, but they also introduce serious security risks. One emerging threat is impersonation: an attacker can steal a user's avatar-preserving their appearance and voice-making it nearly impossible to detect its fraudulent usage by sight or sound alone. In this paper, we explore the challenge of biometric verification in such avatar-mediated scenarios. Our main question is whether an individual's facial motion patterns can serve as reliable behavioral biometrics to verify their identity when the avatar's visual appearance is a facsimile of its owner. To answer this question, we introduce a new dataset of realistic avatar videos created using a state-of-the-art one-shot avatar generation model, GAGAvatar, with genuine and impostor avatar videos. We also propose a lightweight, explainable spatio-temporal Graph Convolutional Network architecture with temporal attention pooling, that uses only facial landmarks to model dynamic facial gestures. Experimental results demonstrate that facial motion cues enable meaningful identity verification with AUC values approaching 80%. The proposed benchmark and biometric system are available for the research community in order to bring attention to the urgent need for more advanced behavioral biometric defenses in avatar-based communication systems.
Abstract (translated)
逼真的说话头像在虚拟会议、游戏和社交平台中越来越常见。这些头像能够促进更沉浸式的交流，但同时也带来了严重的安全风险。其中一项新兴威胁是冒充：攻击者可以窃取用户的头像——保留其外观和声音——使得仅凭视觉或听觉难以辨别其欺诈性使用。在这篇论文中，我们探讨了在这种以头像为媒介的情境下进行生物特征验证的挑战。我们的主要问题是，个人的面部运动模式是否能够作为可靠的生物行为标志来验证身份，即使当头像的外观与其所有者完全相同。为了回答这个问题，我们引入了一个新的数据集，该数据集包含使用最先进的单次生成模型GAGAvatar创建的真实和冒充者的逼真头像视频。此外，我们还提出了一种轻量级且可解释的空间-时间图卷积网络架构，并结合了时序注意力池化机制，仅利用面部地标来建模动态面部手势。实验结果表明，面部运动线索能够实现有意义的身份验证，其AUC值接近80%。所提出的基准和生物特征系统已向研究社区开放，旨在强调在基于头像的通信系统中需要更先进的行为生物识别防御措施。
URL
https://arxiv.org/abs/2508.00748
