用原型签名生成更有效的伪造样本，提升手写签名验证精度
A Prototypical Signature Approach for Writer-Independent Offline Signature Verification
- 用特征压缩的原型签名生成多样伪造样本
- 可识别高技能伪造，准确率显著提升
- 适合作为高效替代RBF模型的方案
离线手写签名验证旨在通过静态图像区分真实与伪造签名。由于真实伪造样本稀缺，训练数据中的负样本通常从其他用户的真实签名中随机选取。但这种随机采样缺乏多样性，导致冗余增加和计算成本上升，影响训练效率。本文提出一种数据驱动策略，利用原型签名生成多样且信息丰富的负样本。原型签名是真实签名特征的紧凑、不可识别摘要。实验表明：(i) 原型签名能生成更具信息量的负样本，提升对高技能伪造的检测能力；(ii) 该方法与主干网络无关，在不同架构下均表现稳健；(iii) 与初等形式线性SVM结合时，可替代基于RBF的模型，显著提升可扩展性和计算效率。代码已开源：https://github.com/kdmoura/proto_hsv。
原文摘要 · Abstract (English)
Offline handwritten signature verification aims to distinguish genuine from forged signatures using static images. Since real forgeries are rarely available, negative samples are usually randomly drawn from genuine signatures of other users to create training data. However, this random selection often lacks diversity, increases redundancy, and escalates computational cost, leading to inefficient training. We propose a data-driven strategy to generate diverse, informative negative samples using prototypical signatures, which are compact, non-identifiable summaries of genuine signature features. Based on the experiments results, we conclude that (i) prototypical signatures yield more informative negative samples, improving the detection of skilled forgeries; (ii) the proposed approach is backbone-agnostic, showing robustness across architectures; and (iii) when combined with a primal-form linear SVM, it serves as an alternative to RBF-based models while significantly improving scalability and computational efficiency. Implementation of the method is available at https://github.com/kdmoura/proto_hsv.
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