# Verification of Dynamic Holographic Behavior in Identity Documents

arXiv: https://arxiv.org/abs/2607.06466 · HTML: https://mlsanity.com/paper/2607.06466/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 7 Jul 2026.

## Builds on (most cited first, 7 of 7)

- [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)
- [MIDV-2019: Challenges of the modern mobile-based document OCR](https://mlsanity.com/paper/1910.04009/) (2019)
- [First Competition on Presentation Attack Detection on ID Card](https://mlsanity.com/paper/2409.00372/) (2024)
- [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)
- [Weakly Supervised Training for Hologram Verification in Identity Documents](https://mlsanity.com/paper/2404.17253/) (2024)

## Abstract (arXiv)

This paper addresses the remote verification of the authenticity of Optically Variable Devices (commonly known as holograms) on identity documents. Typically placed over the cardholder's photo, these devices provide strong and easily verifiable security for human inspection but pose challenges for automated verification. Existing approaches easily cover static frauds (e.g. paper photocopy) and can be evaluated for such, but their capacity to detect real, dynamic fraud cases (e.g. handcrafted hologram) has not been evaluated to date because of the lack of public datasets. Furthermore, they are usually trained to detect known attack types, and few of them can generalize to new, unseen attacks. This work features three contributions to address these limitations: 1) a new public dataset, MIDV-DynAttack, which extends the existing MIDV-Holo dataset with realistic, static and dynamic attacks against identity document specimens, tripling the number of attack samples compared to the original dataset, 2) a novel verification method which can assess the authenticity of a specific hologram thanks to the analysis of its dynamic behavior and appearance, can be trained without dynamic attack samples, and exhibits new state-of-the-art performance, 3) a benchmark of existing approaches which follows a clear evaluation protocol and emphasizes the inability of other approaches to deal with dynamic attacks, as well as new challenging attacks to deal with. Code and dataset are publicly available at https://github.com/EPITAResearchLab/pouliquen.25.icdar.