Summary of Idnet: a Novel Dataset For Identity Document Analysis and Fraud Detection, by Hong Guan et al.
IDNet: A Novel Dataset for Identity Document Analysis and Fraud Detection
by Hong Guan, Yancheng Wang, Lulu Xie, Soham Nag, Rajeev Goel, Niranjan Erappa Narayana Swamy, Yingzhen Yang, Chaowei Xiao, Jonathan Prisby, Ross Maciejewski, Jia Zou
First submitted to arxiv on: 3 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a critical issue in the development of accurate fraud detection tools for government-issued identity documents. Current benchmark datasets are limited in terms of sample size, variety of fraud patterns, and alterations in personal identifying fields like portrait images. As a result, models trained on these datasets struggle to detect realistic frauds while preserving privacy. The paper highlights the importance of developing more comprehensive and diverse datasets for training effective fraud detection tools. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper aims to improve identity document analysis by addressing the limitations of current benchmark datasets. It focuses on creating a dataset that includes various types of fraud patterns, alterations in personal identifying fields, and realistic samples. This will enable the development of more accurate fraud detection models that can detect fraud while protecting privacy. |
