# RoPAD: Robust Presentation Attack Detection through Unsupervised Adversarial Invariance

arXiv: https://arxiv.org/abs/1903.03691 · HTML: https://mlsanity.com/paper/1903.03691/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2019.

## Built on by (most cited first, 3 of 3)

- [Biometrics: Trust, but Verify](https://mlsanity.com/paper/2105.06625/) (2021)
- [Myope Models -- Are face presentation attack detection models short-sighted?](https://mlsanity.com/paper/2111.11127/) (2021)
- [Explaining Face Presentation Attack Detection Using Natural Language](https://mlsanity.com/paper/2111.04862/) (2021)

## Abstract (arXiv)

For enterprise, personal and societal applications, there is now an increasing demand for automated authentication of identity from images using computer vision. However, current authentication technologies are still vulnerable to presentation attacks. We present RoPAD, an end-to-end deep learning model for presentation attack detection that employs unsupervised adversarial invariance to ignore visual distractors in images for increased robustness and reduced overfitting. Experiments show that the proposed framework exhibits state-of-the-art performance on presentation attack detection on several benchmark datasets.