# Camera Invariant Feature Learning for Generalized Face Anti-spoofing

arXiv: https://arxiv.org/abs/2101.10075 · HTML: https://mlsanity.com/paper/2101.10075/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 67 references that are in the map.
- First posted on arXiv 25 Jan 2021.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision](https://mlsanity.com/paper/1803.11097/) (2018)
- [Learning Rich Features for Image Manipulation Detection](https://mlsanity.com/paper/1805.04953/) (2018)
- [Unsupervised Out-of-Distribution Detection by Maximum Classifier Discrepancy](https://mlsanity.com/paper/1908.04951/) (2019)
- [Learning to Compare Image Patches via Convolutional Neural Networks](https://mlsanity.com/paper/1504.03641/) (2015)
- [Learn Convolutional Neural Network for Face Anti-Spoofing](https://mlsanity.com/paper/1408.5601/) (2014)
- [Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection](https://mlsanity.com/paper/1907.04047/) (2019)
- [Face De-Spoofing: Anti-Spoofing via Noise Modeling](https://mlsanity.com/paper/1807.09968/) (2018)
- [Out-of-Distribution Detection for Generalized Zero-Shot Action Recognition](https://mlsanity.com/paper/1904.08703/) (2019)
- [Learning Generalizable and Identity-Discriminative Representations for Face Anti-Spoofing](https://mlsanity.com/paper/1901.05602/) (2019)

## Built on by (most cited first, 9 of 9)

- [Deep Learning for Face Anti-Spoofing: A Survey](https://mlsanity.com/paper/2106.14948/) (2021)
- [Learning Meta Pattern for Face Anti-Spoofing](https://mlsanity.com/paper/2110.06753/) (2021)
- [Dual Spoof Disentanglement Generation for Face Anti-spoofing with Depth Uncertainty Learning](https://mlsanity.com/paper/2112.00568/) (2021)
- [Learnable Multi-level Frequency Decomposition and Hierarchical Attention Mechanism for Generalized Face Presentation Attack Detection](https://mlsanity.com/paper/2109.07950/) (2021)
- [Digital and Physical Face Attacks: Reviewing and One Step Further](https://mlsanity.com/paper/2209.14692/) (2022)
- [M3FAS: An Accurate and Robust MultiModal Mobile Face Anti-Spoofing System](https://mlsanity.com/paper/2301.12831/) (2023)
- [SLIP: Spoof-Aware One-Class Face Anti-Spoofing with Language Image Pretraining](https://mlsanity.com/paper/2503.19982/) (2025)
- [Modeling Spoof Noise by De-spoofing Diffusion and its Application in Face Anti-spoofing](https://mlsanity.com/paper/2401.08275/) (2024)
- [Self-supervised Feature Disentanglement and Augmentation Network for One-class Face Anti-spoofing](https://mlsanity.com/paper/2503.22929/) (2025)

## Abstract (arXiv)

There has been an increasing consensus in learning based face anti-spoofing that the divergence in terms of camera models is causing a large domain gap in real application scenarios. We describe a framework that eliminates the influence of inherent variance from acquisition cameras at the feature level, leading to the generalized face spoofing detection model that could be highly adaptive to different acquisition devices. In particular, the framework is composed of two branches. The first branch aims to learn the camera invariant spoofing features via feature level decomposition in the high frequency domain. Motivated by the fact that the spoofing features exist not only in the high frequency domain, in the second branch the discrimination capability of extracted spoofing features is further boosted from the enhanced image based on the recomposition of the high-frequency and low-frequency information. Finally, the classification results of the two branches are fused together by a weighting strategy. Experiments show that the proposed method can achieve better performance in both intra-dataset and cross-dataset settings, demonstrating the high generalization capability in various application scenarios.