IDNet: A Novel Identity Document Dataset via Few-Shot and Quality-Driven Synthetic Data Generation

Effective fraud detection and analysis of government-issued identity documents, such as passports, driver’s licenses, and identity cards, are essential in thwarting identity theft and bolstering security on online platforms. The accuracy of training fraud detection and analysis tools depends on the availability of extensive and diverse identity document datasets. However, current publicly available benchmark datasets for identity document analysis, including MIDV-500, MIDV-2020, and FMIDV, fall short in several aspects: they offer a limited number of samples of ten European country document types, cover insufficient varieties of fraud patterns, and seldom include alterations in critical personal identifying fields such as portrait images, limiting their utility in training models capable of detecting realistic frauds while preserving privacy. In response to these shortcomings, our research introduces a new benchmark dataset, IDNet, designed to advance privacy-preserving fraud detection efforts, synthesized by integrating the generative models and a Bayesian optimization approach. The IDNet dataset comprises 837, 060 images of synthetically generated identity documents, totaling approximately 490 gigabytes, categorized into 20 types from 10 U.S. states and 10 European countries, which is the largest identity document dataset publicly available today. We evaluated the fidelity and utility of IDNet to demonstrate the effectiveness of our unique synthetic data generation method. We also presented two use cases of the dataset, illustrating how it can aid in training privacy-preserving fraud detection methods, and facilitating the generation of camera and video capturing of identity documents.