# Anomaly Detection-Based Unknown Face Presentation Attack Detection

arXiv: https://arxiv.org/abs/2007.05856 · HTML: https://mlsanity.com/paper/2007.05856/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2020.

## Built on by (most cited first, 20 of 20)

- [Deep Learning for Face Anti-Spoofing: A Survey](https://mlsanity.com/paper/2106.14948/) (2021)
- [One-Class Classification: A Survey](https://mlsanity.com/paper/2101.03064/) (2021)
- [Open-set Adversarial Defense with Clean-Adversarial Mutual Learning](https://mlsanity.com/paper/2202.05953/) (2022)
- [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)
- [SelfMAD: Enhancing Generalization and Robustness in Morphing Attack Detection via Self-Supervised Learning](https://mlsanity.com/paper/2504.05504/) (2025)
- [Myope Models -- Are face presentation attack detection models short-sighted?](https://mlsanity.com/paper/2111.11127/) (2021)
- [SLIP: Spoof-Aware One-Class Face Anti-Spoofing with Language Image Pretraining](https://mlsanity.com/paper/2503.19982/) (2025)
- [Hyp-OC: Hyperbolic One Class Classification for Face Anti-Spoofing](https://mlsanity.com/paper/2404.14406/) (2024)
- [Network Anomaly Detection for IoT Using Hyperdimensional Computing on NSL-KDD](https://mlsanity.com/paper/2503.03031/) (2025)
- [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)
- [Hyperbolic Face Anti-Spoofing](https://mlsanity.com/paper/2308.09107/) (2023)
- [Self-supervised Feature Disentanglement and Augmentation Network for One-class Face Anti-spoofing](https://mlsanity.com/paper/2503.22929/) (2025)
- [Learning Unknown Spoof Prompts for Generalized Face Anti-Spoofing Using Only Real Face Images](https://mlsanity.com/paper/2505.03611/) (2025)
- [A Joint Representation Learning and Feature Modeling Approach for One-class Recognition](https://mlsanity.com/paper/2101.09782/) (2021)
- [A Novel Active Solution for Two-Dimensional Face Presentation Attack Detection](https://mlsanity.com/paper/2212.06958/) (2022)
- [Multi-Modal Face Anti-Spoofing via Cross-Modal Feature Transitions](https://mlsanity.com/paper/2507.05575/) (2025)
- [Introducing Nylon Face Mask Attacks: A Dataset for Evaluating Generalised Face Presentation Attack Detection](https://mlsanity.com/paper/2511.08114/) (2025)
- [Generalized Disguise Makeup Presentation Attack Detection Using an Attention-Guided Patch-Based Framework](https://mlsanity.com/paper/2604.26025/) (2026)

## Abstract (arXiv)

Anomaly detection-based spoof attack detection is a recent development in face Presentation Attack Detection (fPAD), where a spoof detector is learned using only non-attacked images of users. These detectors are of practical importance as they are shown to generalize well to new attack types. In this paper, we present a deep-learning solution for anomaly detection-based spoof attack detection where both classifier and feature representations are learned together end-to-end. First, we introduce a pseudo-negative class during training in the absence of attacked images. The pseudo-negative class is modeled using a Gaussian distribution whose mean is calculated by a weighted running mean. Secondly, we use pairwise confusion loss to further regularize the training process. The proposed approach benefits from the representation learning power of the CNNs and learns better features for fPAD task as shown in our ablation study. We perform extensive experiments on four publicly available datasets: Replay-Attack, Rose-Youtu, OULU-NPU and Spoof in Wild to show the effectiveness of the proposed approach over the previous methods. Code is available at: urlhttps://github.com/yashasvi97/IJCB2020anomaly