# Synthetic dataset of ID and Travel Document

arXiv: https://arxiv.org/abs/2401.01858 · HTML: https://mlsanity.com/paper/2401.01858/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 15 references that are in the map.
- First posted on arXiv 3 Jan 2024.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [TransFG: A Transformer Architecture for Fine-grained Recognition](https://mlsanity.com/paper/2103.07976/) (2021)
- [MIDV-2020: A Comprehensive Benchmark Dataset for Identity Document Analysis](https://mlsanity.com/paper/2107.00396/) (2021)
- [Synthetic ID Card Image Generation for Improving Presentation Attack Detection](https://mlsanity.com/paper/2211.00098/) (2022)

## Built on by (most cited first, 3 of 3)

- [IDNet: A Novel Dataset for Identity Document Analysis and Fraud Detection](https://mlsanity.com/paper/2408.01690/) (2024)
- [FantasyID: A dataset for detecting digital manipulations of ID-documents](https://mlsanity.com/paper/2507.20808/) (2025)
- [Can Foundation Models Generalise the Presentation Attack Detection Capabilities on ID Cards?](https://mlsanity.com/paper/2506.05263/) (2025)

## Abstract (arXiv)

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 trained state-of-the-art models on this dataset and we compare them to the performance achieved in larger, but private, datasets. The creation of this dataset will help to document image analysis community to progress in the task of ID document verification.