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Identifying forged seal imprints using positive and unlabeled learning

  • 1162: Machine learning for big multimedia analytics
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Abstract

Nowadays with the development of photosensitive seal technology, the seal fraud events have gradually increased. Forged seals can bring considerable benefits to counterfeiters, and will also bring huge losses to companies and users. Since it is almost impossible to collect enough forged seal samples, traditional machine learning methods do not work in this situation. In this paper, a method based on PU learning and distance learning is proposed. This method uses a limited number of labeled samples and some unlabeled samples to train multiple kNN classifiers to identify forged seal imprints, and use distance learning to improve the performance of kNN classifiers. The experimental results show that the F1-score of the proposed method can reach 0.97 regardless of the seal imprints with lots of text background noise, which outweighs many traditional models.

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Acknowledgements

This work is supported by the NSFC [grant numbers 61772281, 61703212], the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) and Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET).

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Authors and Affiliations

  1. School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing, China

    Leiming Yan, Kai Chen, Shikun Tong & Jinwei Wang

  2. Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing, China

    Leiming Yan, Kai Chen, Shikun Tong & Jinwei Wang

  3. JiangSu QunJie IOT Technology Co., Ltd, Nanjing, China

    Zhen Chen

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  1. Leiming Yan
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  2. Kai Chen
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  3. Shikun Tong
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  4. Jinwei Wang
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  5. Zhen Chen
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Correspondence to Jinwei Wang.

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Yan, L., Chen, K., Tong, S. et al. Identifying forged seal imprints using positive and unlabeled learning. Multimed Tools Appl 80, 30761–30773 (2021). https://doi.org/10.1007/s11042-020-10171-6

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  • DOI: https://doi.org/10.1007/s11042-020-10171-6

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