# Flickr-PAD: New Face High-Resolution Presentation Attack Detection Database

arXiv: https://arxiv.org/abs/2304.13015 · HTML: https://mlsanity.com/paper/2304.13015/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 25 Apr 2023.

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

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Albumentations: fast and flexible image augmentations](https://mlsanity.com/paper/1809.06839/) (2018)
- [Single-Side Domain Generalization for Face Anti-Spoofing](https://mlsanity.com/paper/2004.14043/) (2020)
- [Deep Learning for Face Anti-Spoofing: A Survey](https://mlsanity.com/paper/2106.14948/) (2021)
- [Learnable Multi-level Frequency Decomposition and Hierarchical Attention Mechanism for Generalized Face Presentation Attack Detection](https://mlsanity.com/paper/2109.07950/) (2021)

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

- [Foundation Models for Face Presentation Attack Detection: A Unified Linear-Probing Benchmark](https://mlsanity.com/paper/2607.26993/) (2026)
- [LoRA-based Adaptation Alone Is Not Enough: Understanding the Limits of Foundation Models for Face Presentation Attack Detection](https://mlsanity.com/paper/2608.09633/) (2026)

## Abstract (arXiv)

Nowadays, Presentation Attack Detection is a very active research area. Several databases are constituted in the state-of-the-art using images extracted from videos. One of the main problems identified is that many databases present a low-quality, small image size and do not represent an operational scenario in a real remote biometric system. Currently, these images are captured from smartphones with high-quality and bigger resolutions. In order to increase the diversity of image quality, this work presents a new PAD database based on open-access Flickr images called: "Flickr-PAD". Our new hand-made database shows high-quality printed and screen scenarios. This will help researchers to compare new approaches to existing algorithms on a wider database. This database will be available for other researchers. A leave-one-out protocol was used to train and evaluate three PAD models based on MobileNet-V3 (small and large) and EfficientNet-B0. The best result was reached with MobileNet-V3 large with BPCER10 of 7.08% and BPCER20 of 11.15%.