Spatio-temporal deep learning for improved face presentation attack detection

doi:10.1016/j.knosys.2025.113059

2025-02-01

PRE

Shujaat Khan *

Taha Hasan Masood Siddique

Muhammad Sohail Ibrahim

黄

黄科杰 (Huang, Kejie)

DOI：10.1016/j.knosys.2025.113059

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En 中文

面呈现攻击检测（FacePAD）对于保护人脸识别系统免受打印照片、视频和3D面具等攻击至关重要。现有方法在泛化能力、计算效率以及处理复杂攻击方面往往面临挑战，尤其是在资源受限的环境中。为解决这些挑战，本研究提出了一种基于轻量级CNN架构MobileNetV3的方案，并集成了时空特征提取。所提出的方法能够有效捕捉动态和静态特征，并实现了最先进的性能，包括在Replay-Attack和Replay-Mobile数据集上达到0.0%的等错误率（EER），以及在具有挑战性的ROSE-Youtu数据集上达到0.83%。凭借实时处理能力，模型可在11毫秒内处理256个样本，适合部署在移动和嵌入式平台上。本研究表明，结合时空特征的轻量级架构能够平衡计算效率与准确性，为人脸认证、监控和访问控制等应用中的实用FacePAD系统树立了基准，提升了生物特征安全性。

Keyword:

Face presentation attack detection

Face anti-spoofing

MobileNetV3

Spatio-temporal

Deep learning

期刊

Knowledge-Based Systems

IF:

7.6

论文数:

1.2W

被引数:

4.5W

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暂无机构信息

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