一个高性能自适应融合网络,用于面部防伪检测
Hui Qi1,2,3, Rui Han1, Kaige Duan4
1School of Computer Science and Technology, Taiyuan Normal University, Jinzhong, 030619, China.
Scientific reports
|October 29, 2025
概括
这项研究引入了适应性融合网络,用于面部防伪检测,改善了不同数据集的概括性. 这种新的方法增强了深度特征表示,用于强大的面部活力检测.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 生物识别信息 生物识别信息
背景情况:
- 目前跨领域的面部活力检测模型在概括和深度特征表示方面扎.
- 现有的方法在准确区分真实面孔与跨不同数据集的假冒攻击方面存在局限性.
研究的目的:
- 为面部防伪检测提出一个高性能自适应融合网络.
- 为了提高泛化能力和深度特征表示在跨域面部活力检测.
主要方法:
- 引入了面部深度地图融合机制,与ResNet-18骨干相结合,用于特征提取.
- 使用动态卷积和瓶注意模块设计了一个内容特征提取架构.
- 采用自适应实例规范化,中心差异卷积和对多源域特征和类别的双重对齐的对抗性培训.
主要成果:
- 提出的方法在四个基准数据集 (OULU-NPU,MSU-MFSD,CASIA-FASD,ReReplay Attack) 上显著优于现有的算法.
- 通过有效地减轻数据分布差异,在跨域面部活力检测中表现出更好的性能.
结论:
- 适应融合网络为跨领域面部活力检测提供了一条创新的技术途径.
- 该方法通过克服概括和特征表示的局限性,为面部防伪提供了强大的解决方案.
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