# Domain Generalized Recaptured Screen Image Identification Using SWIN Transformer

arXiv: https://arxiv.org/abs/2407.17170 · HTML: https://mlsanity.com/paper/2407.17170/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 62 references that are in the map.
- First posted on arXiv 24 Jul 2024.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Single-Side Domain Generalization for Face Anti-Spoofing](https://mlsanity.com/paper/2004.14043/) (2020)
- [Deep Learning-based Forgery Attack on Document Images](https://mlsanity.com/paper/2102.00653/) (2021)
- [Two-branch Multi-scale Deep Neural Network for Generalized Document Recapture Attack Detection](https://mlsanity.com/paper/2211.16786/) (2022)
- [Scale Invariant Domain Generalization Image Recapture Detection](https://mlsanity.com/paper/2110.03496/) (2021)
- [Learning Feature Disentanglement and Dynamic Fusion for Recaptured Image Forensic](https://mlsanity.com/paper/2206.06103/) (2022)

## Built on by (most cited first, 1 of 1)

- [Enhancing Image Authenticity Detection: Swin Transformers and Color Frame Analysis for CGI vs. Real Images](https://mlsanity.com/paper/2409.04742/) (2024)

## Abstract (arXiv)

An increasing number of classification approaches have been developed to address the issue of image rebroadcast and recapturing, a standard attack strategy in insurance frauds, face spoofing, and video piracy. However, most of them neglected scale variations and domain generalization scenarios, performing poorly in instances involving domain shifts, typically made worse by inter-domain and cross-domain scale variances. To overcome these issues, we propose a cascaded data augmentation and SWIN transformer domain generalization framework (DAST-DG) in the current research work Initially, we examine the disparity in dataset representation. A feature generator is trained to make authentic images from various domains indistinguishable. This process is then applied to recaptured images, creating a dual adversarial learning setup. Extensive experiments demonstrate that our approach is practical and surpasses state-of-the-art methods across different databases. Our model achieves an accuracy of approximately 82% with a precision of 95% on high-variance datasets.