SealVerifier: Seal Verification System Based on Dual-stream Model

LEI Meng; NING Qiyue; JU Jinjun; ZOU Liang

doi:10.11999/JEIT241059

Volume 47 Issue 7

Jul. 2025

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Article Navigation > Journal of Electronics & Information Technology > 2025 > 47(7): 2308-2319

LEI Meng, NING Qiyue, JU Jinjun, ZOU Liang. SealVerifier: Seal Verification System Based on Dual-stream Model[J]. Journal of Electronics & Information Technology, 2025, 47(7): 2308-2319. doi: 10.11999/JEIT241059

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LEI Meng, NING Qiyue, JU Jinjun, ZOU Liang. SealVerifier: Seal Verification System Based on Dual-stream Model[J]. Journal of Electronics & Information Technology, 2025, 47(7): 2308-2319. doi: 10.11999/JEIT241059

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SealVerifier: Seal Verification System Based on Dual-stream Model

doi: 10.11999/JEIT241059 cstr: 32379.14.JEIT241059

LEI Meng¹,
NING Qiyue¹,
JU Jinjun²,
ZOU Liang^{1, 3
,
,}

1.
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
2.
Training Base, Army Engineering University of PLA, Xuzhou 221004, China
3.
School of Information Science and Technology, University of Science and Technology of China, Hefei 230026, China

Funds: The National Natural Science Foundation of China (62373360, 62473368), The Scientific Innovation 2030 Major Project for New Generation of AI (2020AAA0107300)

Received Date: 2024-12-02
Rev Recd Date: 2025-04-18

Available Online: 2025-05-08

Publish Date: 2025-07-22

Abstract

Abstract

Objective Seals serve a critical legal function in scenarios such as document authentication and contract execution, acting as essential markers of document authenticity and legitimacy. However, the increasing sophistication of seal forgery techniques, driven by advances in digital technology, presents new challenges to existing verification methods. In particular, low-quality or blurred seal images substantially reduce the accuracy and reliability of traditional approaches, limiting their practical utility. To address these limitations, this study proposes SealVerifier, an automatic seal verification system based on a dual-stream model. The method is designed to improve recognition accuracy, generalization ability, and robustness to noise. SealVerifier contributes to the intelligent development of seal verification and offers technical support for secure digital document authentication, thereby facilitating the broader deployment of reliable seal verification technologies. Methods SealVerifier comprises an image enhancement module and a dual-stream verification model, designed to improve the accuracy and robustness of seal authentication. The framework follows a two-stage pipeline: image preprocessing and authenticity verification. In the preprocessing stage, the DeARegNet module is introduced to correct degradation caused by uneven stamping pressure, scanner variability, paper background complexity, and interference from document content. DeARegNet integrates a Denoising Adversarial Network (DAN) and a GeomPix alignment module to enhance seal image clarity and consistency. DAN employs an adversarial training, consisting of a denoiser and a discriminator. The denoiser uses a multi-level residual dense connection module to extract fine-grained features and eliminate noise, thereby improving image resolution. The discriminator enforces denoising reliability by distinguishing between clean and denoised images using an adversarial loss. The GeomPix alignment module exploits geometric characteristics of circular and elliptical seals. It relies on a central pentagram positioning marker and the radial fan-shaped pixel density distribution to achieve high-precision alignment, significantly improving the accuracy and stability of image correction. In the verification stage, a dual-stream architecture combining EfficientNet and Streamlining Vision Transformer (SViT) is employed to extract local detail features and global structural information. EfficientNet performs efficient multi-scale feature extraction via compound scaling, capturing textures, edge contours, and subtle defects. SViT models global dependencies through self-attention mechanisms and enhances feature learning with high-dimensional multilayer perceptrons and denormalization techniques, thereby improving verification accuracy. To improve generalization and reduce inter-domain discrepancies among seal datasets, a Data Distribution Adapter (DDA) and Gradient Reversal Layer (GRL) are incorporated. These components use adversarial training to support the seal authenticity classifier—comprising EfficientNet and SViT—in learning domain-invariant features. This approach enhances robustness and adaptability in diverse application scenarios. Results and Discussions Experimental results demonstrate that the integration of the dual-stream architecture—EfficientNet for local detail extraction and SViT for global structural representation—enables SealVerifier to significantly improve verification accuracy. On a custom Chinese seal dataset comprising 30,699 image pairs, SealVerifier achieved precision, recall, and F1 scores of 91.34%, 96.83%, and 93.57%, respectively, outperforming existing methods (Table 3). The incorporation of a DDA and a dual loss function further reduced distributional discrepancies across seal datasets using adversarial training, enhancing both recognition accuracy and generalization performance (Table 4). Under noise interference, SealVerifier maintained high verification accuracy, confirming its robustness and applicability in real-world scenarios (Table 2). Conclusions This study proposes SealVerifier, a dual-stream model for fully automated seal authenticity verification. A Chinese seal dataset with complex backgrounds is constructed, and nine-fold cross-validation confirms the method’s effectiveness. SealVerifier integrates DeARegNet for image enhancement and combines EfficientNet and SViT to capture both fine-grained details and global semantic features. To address the limitations of conventional Vision Transformer (ViT) models, high-dimensional multilayer perceptrons and denormalization techniques are introduced, improving the model’s capacity to learn complex features and enhancing generalization and robustness. A DDA and dual loss function are also incorporated to mitigate dataset variability, enabling stable classification performance across heterogeneous seal images. Experimental results show that SealVerifier achieves precision, recall, and F1 scores of 91.34%, 96.83%, and 93.57%, respectively, demonstrating its performance advantage in seal verification tasks. Future work explores high-precision alignment strategies for multi-view seal images to further reduce error and improve image correction accuracy under challenging imaging conditions.
- Automatic seal verification,
- Dual-stream model,
- Transformer,
- Data Distribution Adapter (DDA)

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References(31)

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