Offline signature verification (OSV) is a frequently utilized technology in forensics. This paper proposes a new model, DetailSemNet, for OSV. Unlike previous methods that rely on holistic features for pair comparisons, our approach underscores the significance of fine-grained differences for robust OSV. We propose to match local structures between two signature images, significantly boosting verification accuracy. Furthermore, we observe that without specific architectural modifications, transformer-based backbones might naturally obscure local details, adversely impacting OSV performance. To address this, we introduce a Detail Semantics Integrator, leveraging feature disentanglement and re-entanglement. This integrator is specifically designed to enhance intricate details while simultaneously expanding discriminative semantics, thereby augmenting the efficacy of local structural matching. We evaluate our method against leading benchmarks in offline signature verification. Our model consistently outperforms recent methods, achieving state-of-the-art results with clear margins. The emphasis on local structure matching not only improves performance but also enhances the model's interpretability, supporting our findings. Additionally, our model demonstrates remarkable generalization capabilities in cross-dataset testing scenarios. The combination of generalizability and interpretability significantly bolsters the potential of DetailSemNet for real-world applications.

翻译：离线签名验证（OSV）是法医学中一项常用技术。本文提出了一种用于OSV的新模型DetailSemNet。与以往依赖整体特征进行成对比较的方法不同，我们的方法强调细粒度差异对鲁棒OSV的重要性。我们提出匹配两个签名图像间的局部结构，从而显著提升验证准确率。此外，我们观察到，在没有特定架构修改的情况下，基于Transformer的主干网络可能会自然模糊局部细节，对OSV性能产生不利影响。为解决此问题，我们引入了细节语义整合器，利用特征解耦与再耦合技术。该整合器专门设计用于增强精细细节，同时扩展判别性语义，从而提升局部结构匹配的效能。我们在离线签名验证的主流基准上评估了本方法。我们的模型始终优于近期方法，以明显优势取得了最先进的结果。对局部结构匹配的强调不仅提升了性能，还增强了模型的可解释性，这支持了我们的发现。此外，在跨数据集测试场景中，我们的模型展现出卓越的泛化能力。泛化性与可解释性的结合显著增强了DetailSemNet在实际应用中的潜力。