# From Vision to Text: A Compact Multimodal Approach for Robust, Cross-Domain Presentation Attack Detection on ID Cards

arXiv: https://arxiv.org/abs/2606.06966 · HTML: https://mlsanity.com/paper/2606.06966/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 31 references that are in the map.
- First posted on arXiv 5 Jun 2026.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Sigmoid Loss for Language Image Pre-Training](https://mlsanity.com/paper/2303.15343/) (2023)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [SmolVLM: Redefining small and efficient multimodal models](https://mlsanity.com/paper/2504.05299/) (2025)
- [Why Warmup the Learning Rate? Underlying Mechanisms and Improvements](https://mlsanity.com/paper/2406.09405/) (2024)
- [Contrastive Localized Language-Image Pre-Training](https://mlsanity.com/paper/2410.02746/) (2024)
- [Synthetic ID Card Image Generation for Improving Presentation Attack Detection](https://mlsanity.com/paper/2211.00098/) (2022)
- [First Competition on Presentation Attack Detection on ID Card](https://mlsanity.com/paper/2409.00372/) (2024)
- [Second Competition on Presentation Attack Detection on ID Card](https://mlsanity.com/paper/2507.20404/) (2025)
- [FantasyID: A dataset for detecting digital manipulations of ID-documents](https://mlsanity.com/paper/2507.20808/) (2025)
- [FakeIDet: Exploring Patches for Privacy-Preserving Fake ID Detection](https://mlsanity.com/paper/2504.07761/) (2025)
- [Can Foundation Models Generalise the Presentation Attack Detection Capabilities on ID Cards?](https://mlsanity.com/paper/2506.05263/) (2025)
- [Identity Card Presentation Attack Detection: A Systematic Review](https://mlsanity.com/paper/2511.06056/) (2025)

## Abstract (arXiv)

Cross-domain shifts challenge Presentation Attack Detection (PAD) on ID Cards, given the restricted data available due to privacy concerns. This work proposes a compact multimodal model, based on new generative and discriminative blocks, which combines visual and textual data for PAD on genuine and synthetic ID images. While multimodal models exhibit strong generalisation after supervised fine-tuning, they fail in zero-shot settings. Our findings underscore that model capacity and real-world data are essential for reliable PAD, while existing synthetic datasets may not reflect real-world challenges. We argue for a re-evaluation of synthetic data as a benchmark and emphasise the need for more realistic, diverse datasets to advance PAD research.