Synthetic dataset of ID and Travel Documents.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2024-12-18 DOI:10.1038/s41597-024-04160-9

Carlos Boned, Maxime Talarmain, Nabil Ghanmi, Guillaume Chiron, Sanket Biswas, Ahmad Montaser Awal, Oriol Ramos Terrades

引用次数: 0

Abstract

This paper presents a new synthetic dataset of ID and travel documents, called SIDTD. The SIDTD dataset is created to help training and evaluating forged ID documents detection systems. Such a dataset has become a necessity as ID documents contain personal information and a public dataset of real documents can not be released. Moreover, forged documents are scarce, compared to legit ones, and the way they are generated varies from one fraudster to another resulting in a class of high intra-variability. In this paper we introduce a dataset, synthetically generated, that simulates the most common, and easiest, forgeries to be made by common users of ID documents and travel documents. The creation of this dataset will help to document image analysis community to progress in the task of automatic ID document verification in online onboarding systems.

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.