# IDSPACE: A Novel Document Generator for Reliable Evaluation of Digital Identity Verification Systems [Extended Technical Report]

arXiv: https://arxiv.org/abs/2609.03052 · HTML: https://mlsanity.com/paper/2609.03052/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 44 references that are in the map.
- First posted on arXiv 2 Sep 2026.

## Builds on (most cited first, 18 of 18)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization](https://mlsanity.com/paper/1603.06560/) (2016)
- [Deep Image Blending](https://mlsanity.com/paper/1910.11495/) (2019)
- [MIDV-500: A Dataset for Identity Documents Analysis and Recognition on Mobile Devices in Video Stream](https://mlsanity.com/paper/1807.05786/) (2018)
- [MIDV-2020: A Comprehensive Benchmark Dataset for Identity Document Analysis](https://mlsanity.com/paper/2107.00396/) (2021)
- [Can You Rely on Your Model Evaluation? Improving Model Evaluation with Synthetic Test Data](https://mlsanity.com/paper/2310.16524/) (2023)
- [Synthetic ID Card Image Generation for Improving Presentation Attack Detection](https://mlsanity.com/paper/2211.00098/) (2022)
- [IDNet: A Novel Dataset for Identity Document Analysis and Fraud Detection](https://mlsanity.com/paper/2408.01690/) (2024)
- [DocXPand-25k: a large and diverse benchmark dataset for identity documents analysis](https://mlsanity.com/paper/2407.20662/) (2024)
- [Reducing Geographic Performance Differential for Face Recognition](https://mlsanity.com/paper/2002.12093/) (2020)
- [A large-scale study of performance and equity of commercial remote identity verification technologies across demographics](https://mlsanity.com/paper/2409.12318/) (2024)
- [Can Generative Models Actually Forge Realistic Identity Documents?](https://mlsanity.com/paper/2601.00829/) (2025)
- [DP-DocLDM: Differentially Private Document Image Generation using Latent Diffusion Models](https://mlsanity.com/paper/2508.04208/) (2025)

## Abstract (arXiv)

As services move online, trust institutions such as banks, lenders, and governments must verify the identity of remote users. Fraud detection tools are widely available, but evaluating and fine-tuning them remains difficult because identity documents are sensitive and therefore scarce. Synthetic data generation offers a path forward, and demand is clear: our prior work in this area has been downloaded over 11,000 times (aggregated from eight parts). We introduce IDSpace, extending this line of research in three directions. First, we propose model-guided Bayesian optimization, which tunes generation parameters to maximize both visual similarity and prediction consistency with target-domain models given only a few samples from a target domain. Second, we decouple user-specified metadata (demographics, fraud patterns, capture device) from automatically tuned control parameters (font styles, noise levels, image quality), allowing users to configure evaluations without low-level expertise. Third, we expand beyond template images to support scanned and mobile-captured documents. Experiments show IDSpace improves evaluation consistency by 15-45% over baselines including CycleGAN, diffusion inpainting, and non-guided optimization, using only a few real samples, while improving training accuracy by up to 9% and SSIM similarity with the target domain by 10%. We also released a new dataset consisting of 359,240 high-quality synthetic documents across ten European ID types.