Enhanced Bank Check Security: Introducing a Novel Dataset and Transformer-Based Approach for Detection and Verification
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866929392360357888 |
|---|---|
| author | Khan, Muhammad Saif Ullah Shehzadi, Tahira Noor, Rabeya Stricker, Didier Afzal, Muhammad Zeshan |
| author_facet | Khan, Muhammad Saif Ullah Shehzadi, Tahira Noor, Rabeya Stricker, Didier Afzal, Muhammad Zeshan |
| contents | Automated signature verification on bank checks is critical for fraud prevention and ensuring transaction authenticity. This task is challenging due to the coexistence of signatures with other textual and graphical elements on real-world documents. Verification systems must first detect the signature and then validate its authenticity, a dual challenge often overlooked by current datasets and methodologies focusing only on verification. To address this gap, we introduce a novel dataset specifically designed for signature verification on bank checks. This dataset includes a variety of signature styles embedded within typical check elements, providing a realistic testing ground for advanced detection methods. Moreover, we propose a novel approach for writer-independent signature verification using an object detection network. Our detection-based verification method treats genuine and forged signatures as distinct classes within an object detection framework, effectively handling both detection and verification. We employ a DINO-based network augmented with a dilation module to detect and verify signatures on check images simultaneously. Our approach achieves an AP of 99.2 for genuine and 99.4 for forged signatures, a significant improvement over the DINO baseline, which scored 93.1 and 89.3 for genuine and forged signatures, respectively. This improvement highlights our dilation module's effectiveness in reducing both false positives and negatives. Our results demonstrate substantial advancements in detection-based signature verification technology, offering enhanced security and efficiency in financial document processing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_14370 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhanced Bank Check Security: Introducing a Novel Dataset and Transformer-Based Approach for Detection and Verification Khan, Muhammad Saif Ullah Shehzadi, Tahira Noor, Rabeya Stricker, Didier Afzal, Muhammad Zeshan Computer Vision and Pattern Recognition Automated signature verification on bank checks is critical for fraud prevention and ensuring transaction authenticity. This task is challenging due to the coexistence of signatures with other textual and graphical elements on real-world documents. Verification systems must first detect the signature and then validate its authenticity, a dual challenge often overlooked by current datasets and methodologies focusing only on verification. To address this gap, we introduce a novel dataset specifically designed for signature verification on bank checks. This dataset includes a variety of signature styles embedded within typical check elements, providing a realistic testing ground for advanced detection methods. Moreover, we propose a novel approach for writer-independent signature verification using an object detection network. Our detection-based verification method treats genuine and forged signatures as distinct classes within an object detection framework, effectively handling both detection and verification. We employ a DINO-based network augmented with a dilation module to detect and verify signatures on check images simultaneously. Our approach achieves an AP of 99.2 for genuine and 99.4 for forged signatures, a significant improvement over the DINO baseline, which scored 93.1 and 89.3 for genuine and forged signatures, respectively. This improvement highlights our dilation module's effectiveness in reducing both false positives and negatives. Our results demonstrate substantial advancements in detection-based signature verification technology, offering enhanced security and efficiency in financial document processing. |
| title | Enhanced Bank Check Security: Introducing a Novel Dataset and Transformer-Based Approach for Detection and Verification |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.14370 |