DetailSemNet: Elevating Signature Verification through Detail-Semantic Integration

2025-11-20

AI Summary

Existing offline signature verification (OSV) methods overly rely on global feature matching, neglecting fine-grained discriminative cues, while standard Transformer backbones tend to weaken local structural patterns, limiting both discriminability and interpretability. To address these issues, we propose DetailSemNet: a novel architecture featuring a detail-semantic fusion module that enables feature disentanglement and re-coupling—preserving stroke-level details while enhancing semantic representation; and a local structural matching mechanism that explicitly models geometric deformation and consistency within critical signature regions. Crucially, our method augments fine-grained modeling capability without modifying the underlying Transformer backbone. Extensive experiments demonstrate state-of-the-art performance on major OSV benchmarks—including CEDAR, Bengali, and Chinese—and significantly superior cross-dataset generalization compared to existing approaches.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Feature Construction/Reformulation
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Enhancing signature verification by integrating fine-grained detail semantics
• Addressing transformer-based backbones' tendency to obscure local signature details
• Improving local structure matching for robust offline signature verification

Innovation

• Matching local structures between signature images
• Introducing Detail Semantics Integrator for feature disentanglement
• Enhancing intricate details while expanding discriminative semantics