# DetailSemNet: Elevating Signature Verification through Detail-Semantic Integration

arXiv: https://arxiv.org/abs/2511.16364 · HTML: https://mlsanity.com/paper/2511.16364/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 49 references that are in the map.
- First posted on arXiv 20 Nov 2025.

## Builds on (most cited first, 24 of 24)

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- [DeepFace-EMD: Re-ranking Using Patch-wise Earth Mover's Distance Improves Out-Of-Distribution Face Identification](https://mlsanity.com/paper/2112.04016/) (2021)
- [SURDS: Self-Supervised Attention-guided Reconstruction and Dual Triplet Loss for Writer Independent Offline Signature Verification](https://mlsanity.com/paper/2201.10138/) (2022)
- [Preserving Locality in Vision Transformers for Class Incremental Learning](https://mlsanity.com/paper/2304.06971/) (2023)
- [Multiscale Feature Learning Using Co-Tuplet Loss for Offline Handwritten Signature Verification](https://mlsanity.com/paper/2308.00428/) (2023)
- [DeepFirearm: Learning Discriminative Feature Representation for Fine-grained Firearm Retrieval](https://mlsanity.com/paper/1806.02984/) (2018)

## Abstract (arXiv)

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.