Abstract
Handwriting verification has stood as a steadfast identity authentication method for decades. However, this technique risks potential privacy breaches due to the inclusion of personal information in handwritten biometrics such as signatures. To address this concern, we propose using the Random Digit String (RDS) for privacy-preserving handwriting verification. This approach allows users to authenticate themselves by writing an arbitrary digit sequence, effectively ensuring privacy protection. To evaluate the effectiveness of RDS, we construct a new HRDS4BV dataset composed of online naturally handwritten RDS. Unlike conventional handwriting, RDS encompasses unconstrained and variable content, posing significant challenges for modeling consistent personal writing style. To surmount this, we propose the Pattern Attentive VErification Network (PAVENet), along with a Discriminative Pattern Mining (DPM) module. DPM adaptively enhances the recognition of consistent and discriminative writing patterns, thus refining handwriting style representation. Through comprehensive evaluations, we scrutinize the applicability of online RDS verification and showcase a pronounced outperformance of our model over existing methods. Furthermore, we discover a noteworthy forgery phenomenon that deviates from prior findings and discuss its positive impact in countering malicious impostor attacks. Substantially, our work underscores the feasibility of privacy-preserving biometric verification and propels the prospects of its broader acceptance and application.
Abstract (translated)
几十年来，手写验证一直是一种可靠的身份认证方法。然而，由于在诸如签名等手写生物特征中包含个人信息，这种方法存在潜在的隐私泄露风险。为了解决这一问题，我们提出使用随机数字字符串（RDS）来进行保护隐私的手写验证。通过这种方式，用户可以凭书写任意一串数字来完成身份验证，从而有效地保障了个人隐私安全。为了评估RDS的有效性，我们构建了一个新的HRDS4BV数据集，该数据集中包含了在线自然手写的随机数字字符串。与传统手写不同的是，RDS包含不受约束且可变的内容，这对手写风格的一致性建模提出了重大挑战。 为克服这些困难，我们提出了一种模式注意验证网络（PAVENet），并引入了一个区别式模式挖掘模块（DPM）。该模块能够自适应地增强对一致性和区分性强的书写模式的识别能力，从而优化手写样式的表示。通过全面评估，在线RDS验证的应用性得到了检验，并且我们的模型在现有方法中展现了显著优势。 此外，我们还发现了一种值得注意的伪造现象，这一现象偏离了以往的研究结果，并讨论了它对恶意冒充攻击的正面影响。总体而言，这项工作强调了隐私保护生物特征认证的可能性，并推动了其更广泛接受和应用的发展前景。
URL
https://arxiv.org/abs/2503.12786
