两阶段验证：手部验证码防机器人，指纹识别防伪造。
Two-Stage Human Verification using HandCAPTCHA and Anti-Spoofed Finger Biometrics with Feature Selection
- 先用手部图像验证码过滤机器人，再做指纹生物识别。
- 指纹识别准确率98%~99.5%，误接受率低至5.18%。
- 适合高安全需求场景，如金融、政务系统登录。
本文提出一种两阶段人类验证方案，以应对攻击漏洞并提升安全性。第一阶段通过手部图像验证码（HandCAPTCHA）抵御自动化机器人攻击；第二阶段对通过验证码的用户进行指纹生物特征验证，并结合呈现攻击检测（PAD）技术，利用真实手部图像进行检测。采用屏幕显示的PAD方法，基于图像质量指标评估。随后从四指（不包括拇指）提取几何特征，设计改进的前向后向算法（M-FoBa）进行特征选择。实验在博兹贾吉大学（BU）和印度理工学院德里分校（IITD）手部数据库上进行，使用k近邻和随机森林分类器。正确解答手部验证码的平均准确率为98.5%，机器人误接受率为1.23%。在BU的255名受试者上，PAD平均错误率为0%。针对BU的500名受试者，指纹识别准确率达98%，等错误率（EER）为6.5%；针对IITD的200名受试者，识别准确率为99.5%，EER为5.18%。
原文摘要 · Abstract (English)
This paper presents a human verification scheme in two independent stages to overcome the vulnerabilities of attacks and to enhance security. At the first stage, a hand image-based CAPTCHA (HandCAPTCHA) is tested to avert automated bot-attacks on the subsequent biometric stage. In the next stage, finger biometric verification of a legitimate user is performed with presentation attack detection (PAD) using the real hand images of the person who has passed a random HandCAPTCHA challenge. The electronic screen-based PAD is tested using image quality metrics. After this spoofing detection, geometric features are extracted from the four fingers (excluding the thumb) of real users. A modified forward-backward (M-FoBa) algorithm is devised to select relevant features for biometric authentication. The experiments are performed on the Bogazici University (BU) and the IIT-Delhi (IITD) hand databases using the k-nearest neighbor and random forest classifiers. The average accuracy of the correct HandCAPTCHA solution is 98.5%, and the false accept rate of a bot is 1.23%. The PAD is tested on 255 subjects of BU, and the best average error is 0%. The finger biometric identification accuracy of 98% and an equal error rate (EER) of 6.5% have been achieved for 500 subjects of the BU. For 200 subjects of the IITD, 99.5% identification accuracy, and 5.18% EER are obtained.
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。