# Multi-modal Document Presentation Attack Detection With Forensics Trace Disentanglement

arXiv: https://arxiv.org/abs/2404.06663 · HTML: https://mlsanity.com/paper/2404.06663/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 10 Apr 2024.

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

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [BEiT: BERT Pre-Training of Image Transformers](https://mlsanity.com/paper/2106.08254/) (2021)
- [Searching Central Difference Convolutional Networks for Face Anti-Spoofing](https://mlsanity.com/paper/2003.04092/) (2020)
- [Multi-Modal Face Anti-Spoofing Based on Central Difference Networks](https://mlsanity.com/paper/2004.08388/) (2020)
- [Rethinking Vision Transformer and Masked Autoencoder in Multimodal Face Anti-Spoofing](https://mlsanity.com/paper/2302.05744/) (2023)
- [Synthetic ID Card Image Generation for Improving Presentation Attack Detection](https://mlsanity.com/paper/2211.00098/) (2022)
- [Two-branch Multi-scale Deep Neural Network for Generalized Document Recapture Attack Detection](https://mlsanity.com/paper/2211.16786/) (2022)

## Built on by (most cited first, 2 of 2)

- [Identity Card Presentation Attack Detection: A Systematic Review](https://mlsanity.com/paper/2511.06056/) (2025)
- [From Forgeries to Foundation Models: A Systematic Survey of Identity Document Attack and Detection](https://mlsanity.com/paper/2607.01442/) (2026)

## Abstract (arXiv)

Document Presentation Attack Detection (DPAD) is an important measure in protecting the authenticity of a document image. However, recent DPAD methods demand additional resources, such as manual effort in collecting additional data or knowing the parameters of acquisition devices. This work proposes a DPAD method based on multi-modal disentangled traces (MMDT) without the above drawbacks. We first disentangle the recaptured traces by a self-supervised disentanglement and synthesis network to enhance the generalization capacity in document images with different contents and layouts. Then, unlike the existing DPAD approaches that rely only on data in the RGB domain, we propose to explicitly employ the disentangled recaptured traces as new modalities in the transformer backbone through adaptive multi-modal adapters to fuse RGB/trace features efficiently. Visualization of the disentangled traces confirms the effectiveness of the proposed method in different document contents. Extensive experiments on three benchmark datasets demonstrate the superiority of our MMDT method on representing forensic traces of recapturing distortion.