用于指纹呈现攻击检测的级联伪造挖掘网络

中文摘要 AI 辅助

指纹呈现攻击检测（PAD）是指纹识别系统的关键部分，用于防范未经授权的访问。本文观察到指纹图像不同区域的伪像提取难度（AED）不同，高AED区域需更复杂提取机制。为此，提出用局部Gabor特征确定性量化AED并分区。进而提出AED引导的级联伪造挖掘网络（CFM-Net），采用自适应深度特征提取架构检测不同AED值区域的伪像证据。还引入方向引导对抗训练（OGAT）模块过滤身份信息。在LivDet数据集上的实验表明该方法优于现有方法，对高AED指纹分类能力有显著提升。

英文摘要

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 fingerprint image can exhibit varying Artifact Extraction Difficulty (AED), with high-AED regions requiring more sophisticated extraction mechanisms to capture more subtle discriminative evidence. To address this issue, we propose to quantify AED using local Gabor feature certainty and partition fingerprint images into multiple regions based on their respective AED values. We then propose an AED guided Cascade Forgery Mining Network (CFM-Net) that employs an adaptive-depth feature extraction architecture to detect more precise and comprehensive artifact evidence across regions with heterogeneous AED values. Furthermore, we introduce an Orientation Guided Adversarial Training (OGAT) module to filter out identity information from PAD features while preserving the integrity of original artifact evidence. Experimental evaluations on LivDet datasets demonstrate the superior performance of our approach compared to state-of-the-art methods and achieve significant improvement in the classification ability of high AED fingerprints.

发表机构

• Peking University（北京大学）

机构由 AI 辅助整理，请以论文原文为准。