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English(EN) Cascade Forgery Mining Network for Fingerprint Presentation Attack Detection

新网络利用伪影难度分析改进指纹欺骗检测

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种通过分析具有不同伪影提取难度（AED）的区域来检测假指纹的新方法。他们提出的级联伪造挖掘网络（CFM-Net）使用局部Gabor特征确定性来划分指纹图像，并自适应地从这些区域提取特征。还引入了一个方向引导对抗训练（OGAT）模块来保留伪影证据，同时过滤掉身份信息。在LivDet数据集上的实验表明，CFM-Net的性能优于现有方法，尤其是在具有高AED的指纹上。 AI

影响 这项研究通过提高检测复杂指纹欺骗企图的准确性，有可能增强生物识别系统的安全性。

排序理由 该集群包含一篇详细介绍特定计算机视觉任务新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新网络利用伪影难度分析改进指纹欺骗检测

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该集群包含一篇详细介绍特定计算机视觉任务新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Hongyan Fei, Chuanwei Huang, Zheng Wang, Pengcheng Luo, Jingwei Li, Jufu Feng ·

    用于指纹呈现攻击检测的级联伪造挖掘网络

    arXiv:2607.24090v1 Announce Type: new Abstract: Fingerprint Presentation Attack Detection (PAD) is a critical component of fingerprint identification systems, serving as a protective measure against unauthorized access. In this paper, we observe that different regions of a finger…

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