# Deep Anomaly Detection for Generalized Face Anti-Spoofing

arXiv: https://arxiv.org/abs/1904.08241 · HTML: https://mlsanity.com/paper/1904.08241/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2019.

## Built on by (most cited first, 17 of 17)

- [Self-Supervised Video Forensics by Audio-Visual Anomaly Detection](https://mlsanity.com/paper/2301.01767/) (2023)
- [Deep Learning for Face Anti-Spoofing: A Survey](https://mlsanity.com/paper/2106.14948/) (2021)
- [Detection and Continual Learning of Novel Face Presentation Attacks](https://mlsanity.com/paper/2108.12081/) (2021)
- [Perturbation Learning Based Anomaly Detection](https://mlsanity.com/paper/2206.02704/) (2022)
- [Revisiting Tampered Scene Text Detection in the Era of Generative AI](https://mlsanity.com/paper/2407.21422/) (2024)
- [Digital and Physical Face Attacks: Reviewing and One Step Further](https://mlsanity.com/paper/2209.14692/) (2022)
- [Consistency Regularization for Deep Face Anti-Spoofing](https://mlsanity.com/paper/2111.12320/) (2021)
- [Fake It Till You Make It: Towards Accurate Near-Distribution Novelty Detection](https://mlsanity.com/paper/2205.14297/) (2022)
- [Hyp-OC: Hyperbolic One Class Classification for Face Anti-Spoofing](https://mlsanity.com/paper/2404.14406/) (2024)
- [Explaining Face Presentation Attack Detection Using Natural Language](https://mlsanity.com/paper/2111.04862/) (2021)
- [Unknown Face Presentation Attack Detection via Localised Learning of Multiple Kernels](https://mlsanity.com/paper/2204.10675/) (2022)
- [Principles of Designing Robust Remote Face Anti-Spoofing Systems](https://mlsanity.com/paper/2406.03684/) (2024)
- [Deep Learning meets Liveness Detection: Recent Advancements and Challenges](https://mlsanity.com/paper/2112.14796/) (2021)
- [Race Bias Analysis of Bona Fide Errors in face anti-spoofing](https://mlsanity.com/paper/2210.05366/) (2022)
- [Understanding Cross Domain Presentation Attack Detection for Visible Face Recognition](https://mlsanity.com/paper/2111.02548/) (2021)
- [A Novel Active Solution for Two-Dimensional Face Presentation Attack Detection](https://mlsanity.com/paper/2212.06958/) (2022)
- [Generalized Disguise Makeup Presentation Attack Detection Using an Attention-Guided Patch-Based Framework](https://mlsanity.com/paper/2604.26025/) (2026)

## Abstract (arXiv)

Face recognition has achieved unprecedented results, surpassing human capabilities in certain scenarios. However, these automatic solutions are not ready for production because they can be easily fooled by simple identity impersonation attacks. And although much effort has been devoted to develop face anti-spoofing models, their generalization capacity still remains a challenge in real scenarios. In this paper, we introduce a novel approach that reformulates the Generalized Presentation Attack Detection (GPAD) problem from an anomaly detection perspective. Technically, a deep metric learning model is proposed, where a triplet focal loss is used as a regularization for a novel loss coined "metric-softmax", which is in charge of guiding the learning process towards more discriminative feature representations in an embedding space. Finally, we demonstrate the benefits of our deep anomaly detection architecture, by introducing a few-shot a posteriori probability estimation that does not need any classifier to be trained on the learned features. We conduct extensive experiments using the GRAD-GPAD framework that provides the largest aggregated dataset for face GPAD. Results confirm that our approach is able to outperform all the state-of-the-art methods by a considerable margin.