通过分区域检测指纹伪造，提升高难度区域的识别精度。
Cascade Forgery Mining Network for Fingerprint Presentation Attack Detection
- 按局部特征难易度划分指纹区域，分层提取伪造痕迹。
- 在LivDet数据集上显著优于现有方法，高难度区域识别率提升明显。
- 适合需要高安全性的指纹识别系统研发人员使用。
指纹伪造攻击检测（PAD）是防止非法访问的关键环节。本文发现指纹图像不同区域的伪造特征提取难度（AED）存在差异，高AED区域需更复杂的机制捕捉细微判别证据。为此，我们利用局部Gabor特征置信度量化AED，将指纹图像按AED值划分为多个区域，并提出一种面向AED的级联伪造挖掘网络（CFM-Net），采用自适应深度特征提取结构，在异质AED区域中实现更精准、全面的伪造证据检测。此外，引入方向引导对抗训练模块（OGAT），在保留原始伪造证据完整性的同时，有效过滤身份信息。在LivDet数据集上的实验表明，该方法性能优于当前最先进水平，尤其在高AED指纹的分类能力上取得显著提升。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。