# Generalizable Method for Face Anti-Spoofing with Semi-Supervised Learning

arXiv: https://arxiv.org/abs/2206.06510 · HTML: https://mlsanity.com/paper/2206.06510/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 18 references that are in the map.
- First posted on arXiv 13 Jun 2022.

## Builds on (most cited first, 11 of 11)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [Big Self-Supervised Models are Strong Semi-Supervised Learners](https://mlsanity.com/paper/2006.10029/) (2020)
- [Searching Central Difference Convolutional Networks for Face Anti-Spoofing](https://mlsanity.com/paper/2003.04092/) (2020)
- [CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations](https://mlsanity.com/paper/2007.12342/) (2020)
- [Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision](https://mlsanity.com/paper/2202.08360/) (2022)
- [Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection](https://mlsanity.com/paper/1907.04047/) (2019)
- [Learning Generalizable and Identity-Discriminative Representations for Face Anti-Spoofing](https://mlsanity.com/paper/1901.05602/) (2019)
- [Reduced Focal Loss: 1st Place Solution to xView object detection in Satellite Imagery](https://mlsanity.com/paper/1903.01347/) (2019)
- [Enhance the Motion Cues for Face Anti-Spoofing using CNN-LSTM Architecture](https://mlsanity.com/paper/1901.05635/) (2019)
- [Improving Face Anti-Spoofing by 3D Virtual Synthesis](https://mlsanity.com/paper/1901.00488/) (2019)

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

- [IFAST: Weakly Supervised Interpretable Face Anti-spoofing from Single-shot Binocular NIR Images](https://mlsanity.com/paper/2309.17399/) (2023)

## Abstract (arXiv)

Face anti-spoofing has drawn a lot of attention due to the high security requirements in biometric authentication systems. Bringing face biometric to commercial hardware became mostly dependent on developing reliable methods for detecting fake login sessions without specialized sensors. Current CNN-based method perform well on the domains they were trained for, but often show poor generalization on previously unseen datasets. In this paper we describe a method for utilizing unsupervised pretraining for improving performance across multiple datasets without any adaptation, introduce the Entry Antispoofing Dataset for supervised fine-tuning, and propose a multi-class auxiliary classification layer for augmenting the binary classification task of detecting spoofing attempts with explicit interpretable signals. We demonstrate the efficiency of our model by achieving state-of-the-art results on cross-dataset testing on MSU-MFSD, Replay-Attack, and OULU-NPU datasets.