# Weakly Supervised Training for Hologram Verification in Identity Documents

arXiv: https://arxiv.org/abs/2404.17253 · HTML: https://mlsanity.com/paper/2404.17253/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 26 Apr 2024.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://mlsanity.com/paper/2110.02178/) (2021)
- [Captum: A unified and generic model interpretability library for PyTorch](https://mlsanity.com/paper/2009.07896/) (2020)
- [DeepFake Detection Based on the Discrepancy Between the Face and its Context](https://mlsanity.com/paper/2008.12262/) (2020)
- [MIDV-2020: A Comprehensive Benchmark Dataset for Identity Document Analysis](https://mlsanity.com/paper/2107.00396/) (2021)

## Built on by (most cited first, 4 of 4)

- [DocVCE: Diffusion-based Visual Counterfactual Explanations for Document Image Classification](https://mlsanity.com/paper/2508.04233/) (2025)
- [Temporal Modeling of Optically Variable Devices in Identity Documents](https://mlsanity.com/paper/2607.06408/) (2026)
- [Verification of Dynamic Holographic Behavior in Identity Documents](https://mlsanity.com/paper/2607.06466/) (2026)
- [Video Transformer for Remote Identity Document Hologram Detection](https://mlsanity.com/paper/2607.11419/) (2026)

## Abstract (arXiv)

We propose a method to remotely verify the authenticity of Optically Variable Devices (OVDs), often referred to as ``holograms'', in identity documents. Our method processes video clips captured with smartphones under common lighting conditions, and is evaluated on two public datasets: MIDV-HOLO and MIDV-2020. Thanks to a weakly-supervised training, we optimize a feature extraction and decision pipeline which achieves a new leading performance on MIDV-HOLO, while maintaining a high recall on documents from MIDV-2020 used as attack samples. It is also the first method, to date, to effectively address the photo replacement attack task, and can be trained on either genuine samples, attack samples, or both for increased performance. By enabling to verify OVD shapes and dynamics with very little supervision, this work opens the way towards the use of massive amounts of unlabeled data to build robust remote identity document verification systems on commodity smartphones. Code is available at https://github.com/EPITAResearchLab/pouliquen.24.icdar