基于动态唇纹分析的视觉说话人认证安全性增强
Enhancing the Security of Visual Speaker Authentication Based on Dynamic Lip-Print Analysis
把唇部动态建模成可复用的「动态唇纹」判别片段，并用多层动态增强编码器在短时序、少注册数据下做细粒度特征提取，这套思路可直接迁移到唇语识别、说话人认证与活体检测等视觉身份任务。
原文依据 a Multi-Layer Dynamic-Enhanced Encoder is introduced to model fine-grained lip dynamics
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论近年来，基于人脸的身份认证方法正逐步取代各类应用中的传统方法，在提升安全性的同时也带来了更好的用户便利性。然而，这类方法正受到不断演化的 DeepFake 技术的威胁。本文提出一种基于动态唇纹的新型视觉说话人认证（VSA）方法，以提升系统抵御多种攻击的安全性。唇纹是能够刻画用户局部说话习惯的、具有判别性的视位片段。借助这些动态唇纹，该方法可以在无需额外录制用户数据或重新训练模型的情况下扩展提示词集合，从而增强对重放攻击的抵御能力。此外，本文引入多层动态增强编码器对细粒度唇部动态进行建模，以应对数据稀缺的挑战，并确保在时序跨度较短、注册数据有限的场景下仍能实现稳健的特征提取。我们在多个数据集上开展了大量实验，结果表明所提方法在安全性增强与提示词集合可扩展性两方面均有效。
英文原文摘要 1156 字符
In recent years, face-based authentication methods are gradually replacing traditional methods across various applications, offering enhanced security and user convenience. However, these methods are threatened by the continuously evolving DeepFake techniques. In this paper, a novel Visual Speaker Authentication (VSA) approach based on dynamic lip-prints is proposed to improve system security against diverse attacks. The lip-prints are discriminative viseme segments that capture user's localized speaking habits. By leveraging these dynamic lip-prints, this approach can expand the prompt set without requiring additional user-recordings or model retraining, thereby strengthening resilience to replay attacks. Moreover, a Multi-Layer Dynamic-Enhanced Encoder is introduced to model fine-grained lip dynamics, addressing data scarcity challenges and ensuring robust feature extraction even in scenarios with short temporal spans and limited enrollment data. We have carried out extensive experiments on several datasets and the results have demonstrated the effectiveness of the proposed method in both security enhancement and prompt set scalability.