Abstract
Automated signature verification is a critical biometric technique used in banking, identity authentication, and legal documentation. Despite the notable progress achieved by deep learning methods, most approaches in offline signature verification still struggle to generalize across datasets, as variations in handwriting styles and acquisition protocols often degrade performance. This study investigates feature learning strategies for signature forgery detection, focusing on improving cross-dataset generalization -- that is, model robustness when trained on one dataset and tested on another. Using three public benchmarks -- CEDAR, ICDAR, and GPDS Synthetic -- two experimental pipelines were developed: one based on raw signature images and another employing a preprocessing method referred to as shell preprocessing. Several behavioral patterns were identified and analyzed; however, no definitive superiority between the two approaches was established. The results show that the raw-image model achieved higher performance across benchmarks, while the shell-based model demonstrated promising potential for future refinement toward robust, cross-domain signature verification.
Abstract (translated)
自动签名验证是一种在银行、身份认证和法律文件中至关重要的生物识别技术。尽管深度学习方法已取得了显著的进步，但在离线签名验证领域，大多数方法仍然难以跨数据集泛化，因为书写风格的差异以及获取协议的不同往往会导致性能下降。本研究调查了用于签名伪造检测的功能学习策略，并专注于改进跨数据集的泛化能力——即，在一个数据集上训练模型并在另一个数据集上进行测试时的模型鲁棒性。 该研究使用三个公共基准——CEDAR、ICDAR和GPDS合成，开发了两个实验流程：一种基于原始签名图像，另一种采用被称为“壳预处理”的预处理方法。在识别并分析了几种行为模式后，并未明确证明这两种方法中哪一种更优越。结果显示，基于原始图像的模型在所有基准测试中的表现更好，而基于“壳”的模型展示了在未来进行优化以实现稳健、跨领域的签名验证的巨大潜力。
URL
https://arxiv.org/abs/2510.17724
